Particulate Matter Urban-Focused 

Visibility Assessment 

Final Document 
DISCLAIMER 

This document has been prepared by staff from the Office of Air Quality Planning 

and Standards, U.S. Environmental Protection Agency (EPA). Any opinions, findings, 

conclusions, or recommendations are those of the authors and do not necessarily reflect 

the views of the EPA. Questions related to this document should be addressed to Ms.

Vicki Sandiford, U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: 

sandiford.vicki@epa.gov). 

Elements of this report have been provided to the EPA by Abt Associates, Inc. and 

Stratus Consulting Inc. in partial fulfillment of Contract No. EP-D-08-100, Work Assignments 0-

11 and 0-19. Any opinions, findings, conclusions, or recommendations are those of the authors 

and do not necessarily reflect the views of the EPA or its contractors. 
EPA 452/P-10-004 

July 2010 

Particulate Matter 

Urban-Focused Visibility Assessment 

Final Document 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 
This page left intentionally blank. 
i

TABLE OF CONTENTS 

List of Tables ................................................................................................................................ iv

List of Figures .................................................................................................................................v

List of Acronyms/Abbreviations ................................................................................................. vi

1 INTRODUCTION.......................................................................................................... 1-1

1.1 PM NAAQS BACKGROUND ........................................................................................ 1-4

1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW ........................................................... 1-7

1.3 GOALS AND APPROACH .......................................................................................... 1-11

1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT .................................. 1-12

1.4.1 Background ........................................................................................................ 1-12

1.4.2 Selection of Alternative Scenarios for First Draft Assessments ........................ 1-14

1.4.3 Selection of Alternative Scenarios for Second Draft Assessments ................... 1-15

1.5 ORGANIZATION OF DOCUMENT ........................................................................... 1-18

2 URBAN VISIBILITY PREFERENCE STUDIES ...................................................... 2-1

2.1 METHODS USED IN VISIBILITY PREFERENCE STUDIES .................................... 2-2

2.2 DENVER, COLORADO ................................................................................................. 2-3

2.3 VANCOUVER, BRITISH COLUMBIA, CANADA ..................................................... 2-8

2.4 PHOENIX, ARIZONA .................................................................................................. 2-13

2.5 WASHINGTON, DC ..................................................................................................... 2-15

2.5.1 Washington, DC 2001 ........................................................................................ 2-16

2.5.2 Washington, DC, 2009 ....................................................................................... 2-18

2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF CANDIDATE 

PROTECTION LEVELS ............................................................................................... 2-27

3 ESTIMATION OF RECENT PM MASS AND SPECIES CONCENTRATIONS 

AND PM10 LIGHT EXTINCTION .............................................................................. 3-1

3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM CONCENTRATIONS 

AND LIGHT EXTINCTION ........................................................................................... 3-1

3.1.1 PM2.5 and PM10-2.5 ................................................................................................ 3-1

3.1.2 PM10 light extinction ............................................................................................ 3-4

3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY 

ANALYSIS ...................................................................................................................... 3-9

3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM 

 Data .................................................................................................................... 3-10

3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data ... 3-16

3.2.3 Use of Original IMPROVE Algorithm to Estimate PM10 light extinction ........ 3-19

3.3 DETAILED STEPS ....................................................................................................... 3-19

3.3.1 Hourly PM2.5 Component Concentrations ......................................................... 3-19

3.3.2 Hourly PM10-2.5 Concentrations .......................................................................... 3-28
ii

3.3.3 Hourly Relative Humidity Data ......................................................................... 3-29

3.3.4 Calculation of Daylight 1-Hour PM10 Light Extinction .................................... 3-29

3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM10

Light Extinction NAAQS Scenarios and Most Results ..................................... 3-30

3.3.6 Calculation of Daily Maximum 1-Hour PM10 Light Extinction ........................ 3-34

3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS..................................... 3-34

3.4.1 Levels of Estimated PM2.5, PM2.5 Components, PM10-2.5, and 

 Relative Humidity .............................................................................................. 3-34

3.4.2 Levels of Estimated PM10 light extinction ......................................................... 3-37

3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity and 

PM10 light extinction .......................................................................................... 3-43

3.4.4 Tile Plots of Hourly PM10 Light Extinction ....................................................... 3-46

3.4.5 Extinction Budgets for High PM10 Light Extinction Conditions ....................... 3-63

3.5 POLICY RELEVANT BACKGROUND ...................................................................... 3-85

4 PM10 LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS OF JUST 

MEETING SPECIFIC ALTERNATIVE SECONDARY NAAQS ........................... 4-1

4.1 ALTERNATIVE SECONDARY NAAQS BASED ON PM10 LIGHT EXTINCTION AS 

THE INDICATOR ........................................................................................................... 4-1

4.1.1 Indicator and Monitoring Method ........................................................................ 4-1

4.1.2 Alternative Secondary NAAQS Scenarios based on PM10 light extinction ........ 4-2

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on 

Measured PM10 light extinction ........................................................................... 4-2

4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary NAAQS 

Based on Measured PM10 Light Extinction ......................................................... 4-3

4.2 ALTERNATIVE SECONDARY PM2.5 NAAQS BASED ON ANNUAL AND 24-

HOUR PM2.5 MASS ...................................................................................................... 4-10

4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-hour PM2.5 Mass ....... 4-10

4.2.2 Approach to Modeling Conditions If Secondary PM2.5 NAAQS Based on Annual 

and 24-hour PM2.5 Mass Were Just Met............................................................. 4-11

4.3 RESULTS FOR EACH “JUST MEET” ALTERNATIVE SECONDARY NAAQS 

SCENARIO.................................................................................................................... 4-12

5 SUMMARY ........................................................................................................................ 5-1

5.1 URBAN VISIBILITY PREFERENCE STUDIES REANALYSIS ........................ 5-1

5.2 CURRENT VISIBILITY CONDITIONS................................................................ 5-2

5.3 VISIBILITY CONDITIONS FOR ALTERNATIVE SECONDARY PM NAAQS 

SCENARIOS ....................................................................................................... 5-4

6 REFERENCES ................................................................................................................... 6-1
iii

APPENDICES 

A. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of 

PM10 Light Extinction in the 15 Study Areas 

B. Distributions of Estimated PM2.5 and Other Components under Current Conditions 

C. Development of PRB Estimates of PM2.5 Components, PM10-2.5, and PM10 Light 

Extinction 

D. Relationships between PM Mass Concentration and PM10 Light Extinction under 

Current Conditions 

E. Differences in Daily Patterns of Relative Humidity and PM10 Light Extinction between 

Areas and Seasons 

F. Distributions of Maximum Daily Daylight PM10 Light Extinction under “Just Meets” 

Conditions 

G. Additional Information on the Exclusion of Daylight Hours with Relative Humidity 

Greater than 90 Percent 

H. Inter-Year Variability 

I. Daylight Hours 

J. Logit Memorandum 
iv

LIST OF TABLES 

Table 2-1. VAQ of Denver photos substantively misclassified by majority of 

participants .......................................................................................................... 2-6

Table 2-2. Summary of photographs used in British Columbia study ................................ 2-11

Table 2-3. Logit model estimated VAQ values corresponding to various percent 

acceptablilityvalues for the four cities ............................................................... 2-29

Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates by Region ..... 3-7

Table 3-2. Urban Visibility Assessment Study Areas ......................................................... 3-11

Table 3-3. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Tacoma 

Study Area ......................................................................................................... 3-14

Table 3-4. Number of days per quarter in each study area.................................................. 3-16

Table 3-5. Assumed daylight hours by season (Local Standard Time) ............................... 3-30

Table 3-6. Comparison of Meteorological Parameters for Daylight Hours with Relative 

Humidity Greater than 90 Percent and Other Daylight Hours, 

During 2005 -2007 ............................................................................................. 3-32

Table 3-7. Percentage of daily maximum hourly values and individual hourly values of 

daylight PM10 light extinction exceeding CPLs (excluding hours with relative 

humidity greater than 90 percent). ..................................................................... 3-41

Table 3-8. The numbers of common and unique days selected for each of the 15 urban areas 

by the top 10% of daily maximum and the top 2% of all hours form................ 3-80

Table 4-1. Alternative secondary NAAQS Scenarios for PM10 light extinction ................... 4-2

Table 4-2. Current Conditions PM10 light extinction design values for the study areas. ..... 4-5

Table 4-3. Percentage reductions in non-PRB PM10 light extinction required to “just meet” 

the NAAQS scenarios based on measured light extinction (Mm-1). .................... 4-9

Table 4-4. Percentage reductions required in non-PRB PM2.5 mass to “just meet” NAAQS 

scenarios based on annual and 24-hour PM2.5 mass ........................................... 4-12

Table 4-5. PM10 light extinction design values for “just meet” secondary NAAQS scenarios 

based on measured PM10 light extinction (excluding hours with relative humidity 

greater than 90 percent) ..................................................................................... 4-16

Table 4-6. PM10 light extinction design values for “just meet” secondary NAAQS scenarios 

based on PM2.5 mass (excluding hours with relative humidity greater than 90 

percent) .............................................................................................................. 4-17

Table 4-7. Percentage of days with maximum 1-hour daylight PM10 light extinction above 

CPLs for each NAAQS scenario under “just meet” conditions across three years 

(two in the case of Phoenix and Houston) ........................................................ 2-20
v

LIST OF FIGURES 

Figure 1-1. Progression from PM characteristics to PM light extinction that shows the 

modeling approach (shaded light green) as well as the use of direct measurements 

(shaded blue) as alternative ways to estimate PM light extinction. ..................... 1-9

Figure 1-2. Progression from PM light extinction to value of visual air quality (VAQ) ..... 1-10

Figure 2-1. Percent of Denver participants who considered VAQ in each photograph 

“acceptable.” ........................................................................................................ 2-5

Figure 2-2. Photograph time of day information .................................................................... 2-7

Figure 2-3. Denver photograph time of day results (9:00 a.m. photographs eliminated), 

with the broader range (17.7 dv and 24.6 dv) of the 50% acceptability 

criteria shown. ...................................................................................................... 2-8

Figure 2-4. Composite Chilliwack, BC photograph shows VAQ of 14.1 dv and 34 dv. ....... 2-9

Figure 2-5. Percent of BC participants who consider VAQ in each photograph 

“acceptable.” ...................................................................................................... 2-12

Figure 2-6. Reproduction of image with the best VAQ (15 dv) used in the 

Phoenix study. .................................................................................................... 2-14

Figure 2-7. Percent of Phoenix participants who consider VAQ in each image 

“acceptable.” ...................................................................................................... 2-15

Figure 2-8. Reproduction of the image with the best VAQ (8.8 dv) used in the 

Washington, DC study. ...................................................................................... 2-17

Figure 2-9. Percent of 2001 Washington participants who considered VAQ acceptable in 

each image ......................................................................................................... 2-18

Figure 2-10. Percent of 2009 Test 1 study participants who considered VAQ acceptable 

in each image, showing the range of the lower and upper bound of 50% 

acceptability criteria. .......................................................................................... 2-20

Figure 2-11. Combined results of Washington, DC 2001 and 2009 Test 1 (showing 50% 

acceptability criteria from 2009, Test 1). ........................................................... 2-21

Figure 2-12. Comparison of results from Test 1 and Test 2 (Smith and Howell, 2009). ....... 2-22

Figure 2-13. Average visibility ratings for the Washington, DC WinHaze images by 

participants in Tests 1-3 conducted by Smith and Howell 2009). ..................... 2-23

Figure 2-14. Comparison of results from the Smith and Howell (2009) Test 1 and 

Test 3. ................................................................................................................. 2-25

Figure 2-15. Composite results from Smith and Howell (2009) Tests 1 and 3, and Abt 

(2001) Washington, DC pilot study. .................................................................. 2-26

Figure 2-16. Summary of results of urban visibility studies in four cities, showing the 

identified range of the 50% acceptance criteria . ............................................. 2-28

Figure 3-1. Annual average and 24-hour (98th percentile 24-hour concentrations) 

PM2.5 concentrations in μg/m3, 2007. ................................................................... 3-2

Figure 3-2. Reconstructed 24-hour light extinction in U.S. urban areas in 2003 ................... 3-6

Figure 3-3. Isopleth map of annual total reconstructed PM10 light extinction 

based on 2000-2004 IMPROVE data. ................................................................. 3-8

Figure 3-4. January and August monthly average diurnal profiles of PM2.5 components 

derived from the 2004 CMAQ modeling platform, for the Detroit study 

area. .................................................................................................................... 3-18
vi

Figure 3-5. Sequence of steps used to estimate hourly PM2.5 components and PM10 light 

extinction............................................................................................................ 3-23

Figure 3-6. Example from Detroit study area. ...................................................................... 3-25

Figure 3-7. Distribution of PM parameters and relative humidity across the 2005-2007 period, 

by study area ...................................................................................................... 3-35

Figure 3-8. Distributions of estimated daylight 1-hour PM10 light extinction and maximum 

daily daylight 1-hour PM10 light extinction (in Mm-1 units) across the 2005-2007 

period, by study area (excluding hours with relative humidity greater than 90 

percent). ............................................................................................................. 3-39

Figure 3-9. Distributions of 1-hour PM10 light extinction levels by daylight hour (x-axis) 

across the 2005-2007 period, by study area (excluding hours with relative 

humidity greater than 90 percent). ..................................................................... 3-42

Figure 3-10. Distributions of 1-hour relative humidity levels by daylight hour across the 2005-

2007 period, by study area (excluding hours with relative humidity greater than 

90 percent). ........................................................................................................ 3-44

Figure 3-11. Scatter plot of daylight 1-hour relative humidity (percent) vs. reconstructed PM10

light extinction (Mm-1) across the 2005-2007 period, by study area (excluding 

hours with relative humidity greater than 90 percent). ...................................... 3-45

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction ....................................................... 3-48

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-hour PM10 light Extinction and for the Top 2 Percent of Individual Daylight 

Hours .................................................................................................................. 3-65

Figure 3-14. Average PM10 light extinction budgets for the 15 cities for hours in the (a) top 

10 percent of the maximum daily PM10 light extinction and (b) top 2 percent 

of all daylight hours of PM10 light extinction .................................................... 3-81

Figure 4-1. Comparison of daily max and all daylight hour design values for PM10

light extinction ..................................................................................................... 4-6

Figure 4-2. Comparison of required percentage reductions in non-PRB PM10 light extinction 

needed to meet NAAQS scenarios ..................................................................... 4-10

Figure 4-3. Distributions of daily maximum daylight 1-hour PM10 light extinction under two 

“just meet” secondary NAAQS scenarios (excluding hours with relative humidity 

greater than 90 percent) ..................................................................................... 4-14
vii

LIST OF ACRONYMS/ABBREVIATIONS

AQS EPA’s Air Quality System 

BAM Beta Attenuation Mass Monitor 

BC British Columbia 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CASAC Clean Air Scientific Advisory Committee 

CBSA Consolidated Business Statistical Area 

CCN Cloud Condensation Nuclei 

CDPHE Colorado Department of Public Health and Environment 

CMAQ Community Multiscale Air Quality 

CONUS CMAQ simulations covering continental US 

CPL Candidate Protection Level 

CRA Charles River Associates 

CSA Consolidated Statistical Area 

CSN Chemical Speciation Network 

CTM Chemical Transport Model 

DRE Direct Radiative Effects 

dv Deciview 

EPA United States Environmental Protection Agency 

FEM Federal Equivalent Method 

FRM Federal Reference Method 

GEOS Global Scale Air Circulation Model 

IMPROVE Interagency Monitoring of Protected Visual Environment 

ISA Integrated Science Assessment 

Km Kilometer 

LCD Liquid Crystal Display 

LOESS Locally weighted Scatter Plot Smoothing 

Mm Megameter 

MSA Metropolitan Statistical Area 

N Nitrogen 

NAAQS National Ambient Air Quality Standards 

NARSTO North American Research Strategy for Tropospheric Ozone 

NCEA National Center for Environmental Assessment 

NOAA National Oceanic and Atmospheric Administration 
viii

NOx Nitrogen oxides 

NPS National Park Service 

NRC National Research Council 

NWS National Weather Service 

OAQPS Office of Air Quality Planning and Standards 

OAR Office of Air and Radiation 

OMB Office of Management and Budget 

ORD Office of Research and Development 

PA Policy Assessment 

PM Particulate Matter 

PM2.5 Particles with a 50% upper cut-point of 2.5 μm aerodynamic 

diameter and a penetration curve as specified in the Code of 

Federal Regulations. 

PM10 Particles with a 50% upper cut-point of 10± 0.5 μm aerodynamic 

diameter and a penetration curve as specified in the Code of 

Federal Regulations. 

PM10-2.5 Particles with a 50% upper cut-point of 10 μm aerodynamic 

diameter and a lower 50% cut-point of 2.5 μm aerodynamic 

diameter. 

PRB Policy Relevant Background 

REA Risk and Exposure Assessment 

RF Radiative Forcing 

RH Relative Humidity 

SANDWICH Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous 

mass approach 

SEARCH Southeastern Aerosol Research and Characterization Study 

SMOKE Sparse Matrix Operator Kernal Emissions 

S Sulfur 

SO2 Sulfur Dioxide 

SOx Sulfur Oxides 

STP Standard Temperature and Pressure 

TEOM Tapered Element Oscillating Microbalance 

UBC University of British Columbia 

UFVA Urban-Focused Visibility Impact Assessment 

VAQ Visual Air Quality 
1-1 

1 INTRODUCTION

The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

the 2006 national ambient air quality standards (NAAQS) for particulate matter (PM). Sections 

108 and 109 of the Clean Air Act (Act) govern the establishment and periodic review of the 

NAAQS. The NAAQS are to be based on air quality criteria, which are to accurately reflect the 

latest scientific knowledge useful in indicating the kind and extent of identifiable effects on 

public health or welfare that may be expected from the presence of the pollutant in ambient air. 

The EPA Administrator is to promulgate and periodically review, at no later than five-year 

intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such 

pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator 

is to make revisions in the air quality criteria and standards, and to promulgate any new 

standards, as may be appropriate. The Act also requires that an independent scientific review 

committee advise the Administrator as part of this NAAQS review process, a function performed 

by the Clean Air Scientific Advisory Committee (CASAC). 

The current suite of secondary standards for PM2.5 and PM10 were set in 2006 to be 

identical to the primary standards, on the basis that these standards would, in conjunction with 

the Regional Haze Program1, provide appropriate protection to address PM-related welfare 

effects, including visibility impairment, effects on vegetation and ecosystems, materials damage 

and soiling, and effects on climate change (71 FR 61144, October 17, 2006). At that time, the 

EPA revised the level of the 24-hour PM2.5 primary standard to 35 μg/m3 (calculated as a 3-year 

average of the 98th percentile of 24-hour concentrations at each population-oriented monitor), 

retained the level of the PM2.5 annual primary standard at 15 μg/m3 (calculated as the 3-year 

average of the weighted annual mean PM2.5 concentrations from single or multiple communityoriented monitors), and revised the form of the annual PM2.5 primary standard by narrowing the 

constraints on the optional use of spatial averaging2

. With regard to the primary standards for 

PM10, EPA retained the 24-hour PM10 standard at 150 μg/m3

 (not to be exceeded more than once 

per year on average over 3 years) and revoked the annual standard because available evidence 

generally did not suggest a link between long-term exposure to current ambient levels of coarse 

particles and health effects. The 2006 primary standards were based primarily on a large body of 

 

1

 See http://www.epa.gov/air/visibility/program.html for more information on EPA’s Regional Haze 

Program. 

2

 In the revisions to the PM NAAQS finalized in 2006, EPA tightened the constraints on the spatial 

averaging option limiting the conditions under which some areas may average measurements from multiple 

community-oriented monitors to determine compliance (see 71 FR 61165-61167, October 17, 2006). 
1-2 

epidemiological evidence relating ambient PM concentrations to various adverse health 

outcomes. (As noted below, portions of the 2006 decision were reversed and remanded by the 

Court of Appeals for the District of Columbia Circuit.) 

In the Integrated Review Plan for the National Ambient Air Quality Standards for 

Particulate Matter, March 2008 (US EPA, 2008a), developed early in the current review of the 

PM NAAQS,3

 the EPA outlined the science policy questions that frame this review, outlined the 

process and schedule for the review, and provided descriptions of the purpose, contents, and 

approach for developing the key documents that will be developed in the review.4 EPA has 

completed the process of assessing the latest available policy-relevant scientific information to 

inform the review of the PM standards. The final assessment is contained in the final Integrated 

Science Assessment for Particulate Matter (ISA, US EPA, 2009a) which was released in 

December 2009. The final PM ISA includes a summary of the scientific evidence for the 

relationship of PM to visibility effects, remote area and urban haze conditions, the PM 

components responsible for visibility impacts, and studies of public preference with respect to 

urban visibility conditions. 

Building upon the visibility effects evidence presented in the PM ISA, as well as CASAC 

advice (Samet, 2009a and b) and public comments on the plan for and first draft of the UrbanFocused Visibility Assessment (UFVA) (US EPA, 2009b, c), EPA’s Office of Air Quality 

Planning and Standards (OAQPS) developed a second draft UFVA (US EPA, 2010a) which 

described the quantitative assessments conducted by the Agency to support the review of the 

secondary PM standards. This second draft document presented the methods, key results, 

observations, and related uncertainties associated with the quantitative analyses performed and 

was reviewed and discussed by CASAC and the public at a March 10-11 meeting. Based on 

input received at the March 2010 meeting and in a subsequent letter (Samet, 2010a), this final 

UFVA document includes the following changes: 1) inclusion of the complete logit memo in 

Appendix J and streamlining of logit discussion in chapter 2 to reduce redundancy and reflect 

this addition; 2) Figure 3-13 and associated text was modified to provide a more consistent 

comparison of speciated PM mass for the top 10% and 2% of maximum daylight hours and all 

daylight hours, respectively; 3) addition of footnotes and caveats in the text to acknowledge that 

 

3

 See http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html for more information on the current and 

previous PM NAAQS reviews. 4

 On November 30, 2007, EPA held a consultation with the Clean Air Scientific Advisory Committee 

(CASAC) on the draft IRP (Henderson, 2008). Public comments were also requested on the draft plan and 

presented at that CASAC teleconference. The final IRP incorporated comments received from CASAC and the 

general public on the draft plan as well as input from senior Agency managers. CASAC is an independent scientific 

advisory committee established to meet the requirements of section 109(d)(2) of the Clean Air Act. See 

http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/CASAC for more information, and, in particular, 

information on the CASAC PM Review Panel activities.
1-3 

the St. Louis data is now considered unrealistically high and is not carried forward into the 

second draft PA5; 4) modification of Table 3-6 to correctly omit non-daylight hours which were 

inadvertently included in the second draft UFVA, report results for four study areas for which 

results were missing in the second draft UFVA, and separate “mist” from “smoke/haze” to 

reflect that “mist” is a natural condition while “smoke/haze” is not always a natural condition; 

and 5) addition of an integrative summary (chapter 5).

In addition, a preliminary draft PA (US EPA, 2009d) was released in September 2009 for 

informational purposes and to facilitate discussion with CASAC at the October 5-6, 2009 

meeting on the overall structure, areas of focus, and level of detail to be included in the PA. This 

preliminary draft PA was discussed in conjunction with CASAC review of and public comment 

on the second draft ISA, first draft UFVA, and first draft health risk assessment documents 

produced in support of this PM NAAQS rulemaking. CASAC comments on the preliminary 

draft PA were considered in developing the first external review draft PA (US EPA, 2010b). The 

first draft PA, which built upon the information presented in the final ISA and second draft 

UFVA, was released for CASAC review and public comment in February of 2010 (US EPA, 

2010b). EPA presented an overview of the first draft PA at the CASAC meeting on March 10, 

2010. CASAC and public review of the first draft PA was discussed during public 

teleconferences on April 8-9, 2010 (75 FR 8062, February 23, 2010) and May 7, 2010 (75 FR 

19971, April 16, 2010). CASAC (Samet, 2010b) and public comments on the first draft PA were 

considered in developing the second draft PA which will be reviewed by CASAC at an 

upcoming meeting scheduled for July 26-27, 2010 (75 FR 32763), June 9, 2010). 

The PA is intended to help “bridge the gap” between the Agency’s scientific assessments, 

presented in the ISA and UFVA, and the judgments required of the Administrator in determining 

whether it is appropriate to retain or revise the secondary PM standards. The PA is intended to 

provide a transparent staff analysis of the scientific basis for alternative policy options for 

consideration by senior EPA management prior to rulemaking by integrating and interpreting 

information from the ISA and the UFVA to frame policy options and to facilitate CASAC’s 

advice to the Agency and recommendations on any new standards or revisions to existing 

standards as may be appropriate, as provided for in the Clean Air Act. A second draft PA (US 

EPA, 2010c) has been released in conjunction with this final UFVA document. 

 

5

 Comments concerning unrealistically high PM10-2.5 values for St. Louis are viewed as credible, but were 

received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all 

portions of this document. However, the text has been revised to caution readers with respect to the St. Louis 

results, and they are not included in the visibility effects discussion in the second draft PM Policy Assessment 

document. Some graphics have been updated to exclude St. Louis results in this final UFVA. 
1-4 

1.1 PM NAAQS BACKGROUND 

In the review of the secondary PM NAAQS completed in 2006, EPA took into account 

that the Regional Haze Program, authorized under sections 169A and 169B of the CAA, was 

established to address all human-caused visibility impairment in federal Class I areas. The 

national goal of this program is to prevent any future, and remedy any existing, impairment of 

visibility in mandatory class I Federal areas (Class I areas) which results from manmade air 

pollution. This program also mandates that states develop SIPs to ensure that reasonable 

progress is made towards meeting those goals. Because Congress explicitly targeted Class I 

areas for this pristine level of protection, it can be concluded that Congress did not envision such 

a stringent goal in non-Class I areas. See American Trucking Ass’n v. Browner, 175 F. 3d 1027, 

1056-57 (D. C. Cir. 2002) (upholding this position). However, Congress recognized that 

visibility impairment can and often does occur in areas outside federal Class I areas, including 

urban areas and judged that protection from visibility impairment was important in those areas as 

well. In this regard, Congress included visibility effects in the definition of public welfare 

effects that should be protected under the national ambient air quality standards (NAAQS) 

program authorized in sections 108 and 109 of the CAA. As a result, EPA may establish 

secondary standards addressing visibility impairment notwithstanding existence of the Regional 

Haze Program. Under the NAAQS program, it is up to the Administrator to judge the requisite 

level of public welfare visibility protection. 

Recognizing that efforts were underway to provide increased protection to Class I areas 

under the Regional Haze Program, EPA focused the 2006 PM NAAQS review on visibility 

impairment in non-Class I areas. Because most of the available non-Class I PM data came from 

PM monitoring sites located primarily in urban areas, the assessments took on an urban focus. In 

addition, EPA considered available information on people’s preferences for different levels of 

visual air quality which came from studies conducted in urban areas and information regarding 

existing urban visibility programs and goals. 

In an effort to minimize the factors that historically had complicated efforts to address 

visibility impairment nationally, (i.e., the substantial East/West differences in factors 

contributing to impairment in Class I areas), EPA staff noted that with respect to fine particles, 

East/West differences in urban areas are substantially smaller than in rural areas. Further, 

relative humidity levels, though generally higher in eastern than western areas, are appreciably 

lower in both regions during daylight, as compared to nighttime, hours. The PM2.5 data available 

at that time in urban areas were obtained using a filter –based Federal Reference Method (FRM) 

which captures ambient PM2.5 on a filter and then dries it to get the dry PM2.5 mass 

concentration. By drying the sample, most water and to some extent other labile PM compounds 

evaporate so that the original characteristics (e.g., particle size and composition) of the ambient 
1-5 

PM are altered. Using PM and meteorological data from 161 cities, EPA staff assessed the 

correlations between PM2.5 levels and reconstructed (RE) (i.e., calculated) light extinction during 

daylight hours for different regions of the country. This assessment showed that the strongest 

correlation in the relationship of ambient PM light extinction to dry PM2.5 mass concentration 

was during afternoon periods when lower relative humidity conditions generally prevailed in all 

regions of the country and ambient PM was drier (Staff Paper, US EPA, 2005). While EPA 

recognized that the effect of ambient PM on visibility results from the ambient particle 

characteristics of size, concentration, and composition (including associated water) present in the 

air in the sight path of the observer, given the data availability at the time, EPA viewed the FRM 

altered PM2.5 mass concentration as an acceptable indicator for addressing ambient PM-related 

visibility effects at the national scale during afternoon hours. Thus, the 2005 Staff Paper chose 

to address the issue of regional differences in terms of averaging time rather than indicator, 

discussing the use of a sub-daily afternoon dry PM2.5 standard, because the generally lower 

afternoon relative humidity tended to produce a more uniform relationship between light 

extinction and dry PM2.5 mass concentration throughout the country, therefore providing a more 

uniform level of visibility protection nationwide. This more uniform level of visibility 

protection, however, was limited to the afternoon hours of the day when relative humidity and 

visibility impairment tend to be the lowest. 

Based on the above, in the 2005 PM Staff Paper, EPA staff recommended a separate subdaily secondary standard to address visibility impairment using dried PM2.5 mass concentration 

as the indicator, a recommendation endorsed by CASAC. In the 2006 proposal notice, however, 

EPA proposed to revise the secondary standards by making them identical to the suite of 

proposed primary standards for fine and coarse particles, to provide protection against PMrelated public welfare effects including visibility impairment, effects on vegetation and 

ecosystems, materials damage and soiling, and climate, while soliciting comment on adding a 

new sub-daily PM2.5 secondary standard to address visibility impairment primarily in urban areas 

(71 FR 2620). CASAC provided additional advice to EPA in a letter to the Administrator 

requesting reconsideration of CASAC’s recommendations for both the primary and secondary 

PM2.5 standards as well as standards for thoracic coarse particles (Henderson, 2006). With 

regard to the secondary standard, CASAC reaffirmed “... the recommendation of Agency staff 

regarding a separate secondary fine particle standard to protect visibility.... the CASAC wishes 

to emphasize that continuing to rely on primary standards to protect against all PM-related 

adverse environmental and welfare effects assures neglect, and will allow substantial continued 

degradation, of visual air quality over large areas of the country” (Henderson, 2006). 

On September 21, 2006, EPA announced its final decisions to provide increased 

protection of public welfare by making the secondary NAAQS identical to the revised primary 
1-6 

standards (71 FR 61144, October 17, 2006). This suite of secondary standards was designed to 

address both visibility and other non-visibility welfare related effects. Specifically, with regard 

to the secondary welfare effect of visibility impairment, the Administrator believed that revising 

both the 24-hour and annual PM2.5 secondary standards to be identical to the revised suite of 

PM2.5 primary standards was a reasonable policy approach to address visibility impairment 

primarily in urban areas. With regard to the other non-visibility PM-related welfare effects such 

as vegetation and ecosystems, materials damage and soiling, and climate, the Administrator 

concluded that it was appropriate to address these effects by revising the current suite of PM2.5

secondary standards, making them identical in all respects to the suite of primary PM2.5

standards, while retaining the current 24-hour PM10 secondary standard and revoking the current 

annual PM10 secondary standard. In particular for coarse particles, EPA retained PM10 as the 

indicator for purposes of regulating the coarse fraction of PM10 and retained the 24-hour 

secondary PM10 standard at 150 μg/m3 and revoked the annual secondary PM10 standard. 

Several parties filed petitions for review following promulgation of the revised PM 

NAAQS in 2006. These petitions addressed a number of issues, including the decision to set the 

secondary PM2.5 standards identical to the primary standards. On judicial review the court 

remanded the secondary PM2.5 NAAQS to EPA because the Agency failed to adequately explain 

why setting the PM2.5 secondary standards equal to the primary PM2.5 standards provided the 

required protection from visibility impairment. In particular, the Agency failed to identify a 

target level of visibility impairment that would be requisite to protect the public welfare, and 

improperly relied on a misleading comparison of the number of counties which would be in 

nonattainment for the revised primary NAAQS compared to one alternative secondary standard 

under consideration. Among other things, this equivalence analysis failed to address the issue of 

regional differences in humidity-related effects on visibility. American Farm Bureau Federation

v. EPA, 559 F. 3d 512, 530-31 (D.C. Cir. 2009). 

The analyses developed for and described in this document reflect consideration of the 

issues raised by the court. In particular, a) the reanalysis of public preference studies (described 

in Chapter 2) provides information useful for the selection of “target levels” for urban visibility 

protection; b) the analyses of the factors contributing to visibility impairment for selected urban 

areas, including PM species component contributions and variations in relative humidity, provide 

information useful for better characterization of regional differences important for development 

of a national standard (Chapter 3); and c) the analyses of alternative standards (Chapter 4) using 

different combinations of indicator, averaging times, levels and forms provide information useful 

in understanding the degree of visibility protection provided by alternative standards. 
1-7 

1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW 

Light extinction is the loss of light per unit of distance and occurs when light is scattered 

and/or absorbed. Particulate matter and gases can both scatter and absorb light. Light scattering 

by gases (e.g., nitrogen, oxygen, etc.) that comprise the pollutant free or clean atmosphere (also 

known as Rayleigh or clean-air scattering) is related to the density of the air, which is 

sufficiently constant with elevation that it can be taken to be a time invariant constant that 

depends principally on elevation above sea level. NO2 is the only atmospheric pollutant gas that 

absorbs light appreciably and its effects are generally small (i.e., less than 5%) compared to PM 

light extinction. Hereinafter the phrase “PM light extinction” indicates that the Rayleigh 

contribution to light extinction (nominally considered 10 Mm-1) has been subtracted out and the 

NO2 contribution is considered negligible or is simply excluded due to the measurement 

approach used. By contrast, the term “light extinction” or “total light extinction” is meant to 

include both the Rayleigh and NO2 contributions. 

Visual air quality (VAQ) is defined as the visibility effect caused solely by air quality 

conditions and excluding those associated with meteorological conditions like fog and 

precipitation. It is commonly measured as either light extinction (in terms of inverse 

megameters, Mm-1) or the haziness index (in terms of deciview, dv) (Pitchford and Malm, 1993). 

The haziness index measured in deciview units was developed for use in visibility perception 

studies because it has a more linear relationship to perceived changes in haze compared with 

light extinction. It is defined as ten times the natural logarithmic of one tenth of the light 

extinction in inverse megameter units (Mm-1) (Pitchford and Malm, 1993). Light extinction and 

haziness are physical measures of the amount of visibility impairment (e.g., the amount of 

“haze”), with both increasing as the amount of haze increases. Visual range, defined as the 

greatest distance that a large dark object can be seen, was developed for military and 

transportation safety use. Under conditions that meet certain standard assumptions, visual range 

is inversely related to light extinction (Pitchford and Malm, 1993). 

PM is a heterogeneous mixture of particles of different sizes and chemical compositions. 

While visibility impairment has been associated most often with PM2.5, larger particles such as 

those found in PM10 may be a significant contributor in some areas. Thus, the UFVA considers 

the visibility impairment caused by all particles 10 microns or smaller. As stated above, the 

degree of visibility impairment caused by a given mass of PM depends in large part on the size, 

density and chemical composition of the PM. If the ambient PM has a large number of 

hygroscopic particles (i.e., particles that readily absorb moisture from the atmosphere), and also 

occurs when the relative humidity of the air is higher, those particles will grow larger in size and 

have a larger haze effect than if those same particles occurred in ambient air with lower relative 

humidity. 
1-8 

Ambient PM light extinction is most accurately determined by direct measurements. 

However, as shown in Figure 1-1, the ambient PM light extinction can also be estimated from 

dry PM mass and composition data and relative humidity using an algorithm. One well 

established algorithm, known as the IMPROVE algorithm6

, accounts for water present in 

hygroscopic PM components and uses assumed light extinction efficiencies for each of the major 

PM species. Because there is limited ambient PM light extinction data available in urban areas, 

the assessments below will principally use monitored and modeled dry PM mass and species 

estimates, along with relative humidity measurements as input to the IMPROVE algorithm for 

estimating ambient PM light extinction. 

 

6

 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.) 
1-9 

Figure 1-1. Progression from PM Characteristics to PM Light Extinction That Shows the 

Modeling Approach (shaded light green) as well as the Use of Direct 

Measurements (shaded blue) as Alternative Ways to Estimate PM Light 

Extinction 

The extent to which any amount of light extinction affects a person’s ability to view a 

scene depends on both scene and light characteristics. For example the appearance of a nearby 

object (i.e., a building) is generally less sensitive to a change in light extinction than the 

appearance of a similar object at a greater distance. For a scene with known characteristics, the 

degradation in the scene associated with a change in light extinction can be determined and the 

resulting appearance can be realistically displayed on a digital photograph of the scene using the 

WinHaze system7

. Figure 1-2 below shows the progression from PM light extinction to 

perceived visual air quality impacts to the valuation of those perceived impacts. 

 

7

 Molenar, et al., 1994 

Measured Derived 

Dry PM Mass 

Concentration 

Dry PM Composition 

Ambient Relative 

Humidity 

Dry PM

Characteristics

Hygroscopic 

Growth Model 

Ambient PM 

Characteristics 

Ambient PM Light 

Extinction 

Light Extinction 

Model 

Ambient PM Light 

Extinction 
1-10 

Survey studies have used sets of photographs or computer simulated images developed 

from a base photo depicting a range of visibility conditions on urban scenes to assess the 

individual’s opinion on the acceptability of those VAQ conditions. For the specific scenes used 

in such studies there is a known or predetermined one-to-one correspondence between the 

amount of ambient or computer generated haze captured in the photographs or images, 

respectively, and the associated amount of ambient PM light extinction. For visibility preference 

studies, visibility levels are generally characterized using the haze index in units of deciview 

(similar to the decibel scale for sound). 

Figure 1-2. Progression from PM Light Extinction to Value of Improved Visual Air 

Quality (VAQ) 

Relationship Steps from PM 

Light Extinction to VAQ 

Non-Air Quality 

Information 

Ambient PM Light 

Extinction 

Perceived Visual Air 

Quality (Images) 

Scene and Lighting 

Characteristics 

WinHaze 

Modeling 

Public-Scene 

Contextual 

Information 

Value of Improved 

VAQ 

Valuation Studies 
1-11 

1.3 GOALS AND APPROACH 

The goal of the UFVA is to characterize visibility impairment in 15 selected urban areas 

under recent air quality conditions, “just meet” air quality scenarios for both current secondary 

PM2.5 standards, and under scenarios using various alternative standards which utilize different 

indicators, averaging times, forms and levels to identify those that better reflect the relationship 

between ambient PM and visibility impairment. In particular, the UFVA focuses on the use of a 

PM10 light extinction-based indicator for a possible secondary PM NAAQS (see Figure 1-1 and 

1-2). This is done by comparing estimates of hourly PM10 light extinction in 15 major U.S. 

urban areas over a three-year period (2005-2007) to a range of light extinction values, i.e., 

candidate protection levels (CPLs), beyond which half of the participants in assessed urban 

visibility preference studies indicated the haze conditions were unacceptable (see discussion in 

chapter 2 below, Stratus Consulting Inc., 2009 and Appendix J). In addition, the UFVA includes 

additional characterizations of the effectiveness of the current and an alternative suite of PM2.5 

secondary standards8

. 

The previous PM NAAQS review used the results of visibility preference survey studies 

conducted in Denver (1990), Phoenix (2003), and British Columbia (1993) as the basis for 

suggesting that a standard set to protect visibility conditions to a level within a visual range from 

between about 40 km to about 60 km (corresponding to light extinction from ~100 Mm-1 to ~67 

Mm-1) could represent an appropriate degree of welfare protection from PM9. With the 

exception of a small pilot study conducted in Washington, DC in 2001 (9 participants; Abt 

Associates Inc., 2001), and a replicate study also conducted for Washington, DC in 2009 (26 

participants; Smith and Howell, 2009), there are no additional visibility preference survey studies 

upon which to base the selection of CPLs. 

The EPA staff, with contractor support, has conducted a more detailed, in-depth 

assessment of the results from these studies, including the two Washington, DC studies. This 

assessment includes an analysis that combines data from across all studies using graphical and 

logit model analysis to examine the consistency of the results between the surveys (Stratus 

Consulting Inc., 2009 and Appendix J). Based on the results of this analysis, we have been able 

to refine the range of visibility conditions put forth in the 2006 review, that could represent an 

appropriate degree of public welfare visibility protection, and to determine a central tendency 

value for the CPLs. These analyses and results are described below in chapter 2. 

 

8

 EPA also included an assessment of the sub-daily PM2.5 mass concentration indicator, which was 

explored in the 2005 PM staff paper and which was considered a viable option by EPA staff and CASAC in the 

2006 review. These latter assessments are summarized in Appendix D. 

9

 Light extinction is inversely related to visual range. 
1-12 

In the previous PM NAAQS review, the characterization of urban visibility conditions 

were based on IMPROVE algorithm estimates using the 2001 to 2003 PM2.5 mass and speciation 

data from 161 urban areas by assuming a constant composition for every hour of the day equal to 

the 24-hour measured composition and by using either actual or monthly average (10-year mean) 

hour of the day relative humidity. Statistical relationships between hourly light extinction 

estimates and concurrent hourly PM2.5 mass concentrations were used to show that daytime and 

especially afternoon relationships are relatively strong with a similar linear relationship for both 

eastern and western urban areas (i.e. R2>0.6, slope ~6 m2/g). 

The current assessment of urban visibility conditions (as described in chapter 3) uses a 

modeling approach to estimate hourly light extinction using PM2.5 mass and speciation data with 

measured relative humidity. However, it differs by replacing the unrealistic assumption of 

constant composition for PM2.5, with composition that is made to vary during the day using 

urban-specific monthly mean diurnal variations of species concentrations determined from 

regional air quality model results, while constraining the means of the hourly species 

concentration for each day to closely match the 24-hour duration measured species 

concentrations. 

1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT 

This section provides an overview of the scope and key design elements of the UFVA, 

including the process that has been followed to design the analyses. Following initiation of this 

PM NAAQS review in 2007, we began the design of the assessments in the UFVA by revisiting 

the analyses completed during the previous PM NAAQS review (Abt Associates Inc., 2001; US 

EPA, 2005, chapter 6) with an emphasis on considering key limitations and sources of 

uncertainty recognized in that review. 

1.4.1 Background 

As an initial step in this review, EPA invited a wide range of external experts as well as 

EPA staff, representing a variety of areas of expertise to participate in a workshop titled, 

“Workshop to Discuss Policy-Relevant Science to Inform EPA’s Integrated Plan for the Review 

of the Secondary PM NAAQS” (72 FR 34005, June 20, 2007). This workshop provided an 

opportunity for the participants to broadly discuss the key policy-relevant issues around which 

EPA would structure the PM NAAQS review and to discuss the most meaningful new science 

that would be available to inform our understanding of these issues. One session of this 

workshop centered on issues related to visibility impacts associated with ambient PM. 

Specifically, the discussions focused on the extent to which new research and/or improved 

methodologies were available to inform how EPA evaluated visibility impairment in this review. 
1-13 

Based in part on these workshop discussions, EPA developed a draft IRP outlining the 

schedule, the process, and the key policy-relevant science issues that would guide the evaluation 

of the air quality criteria for PM and the review of the primary and secondary PM NAAQS, 

including initial thoughts for conducting quantitative assessments (US EPA, 2007, chapter 6). 

On November 30, 2007, CASAC held a teleconference with EPA to provide its comments on the 

draft IRP (72 FR 63177, November 8, 2007). Public comments were also presented at that 

teleconference. A final IRP incorporating comments received from CASAC and the general 

public on the draft plan was issued in March 2008 (US EPA, 2008a). 

In articulating a rationale for the urban focus of this assessment, we reviewed the 

available information and found the following information compelling: 1) PM levels in urban 

areas are often in excess of those of the surrounding region since urban haze typically includes 

both regional and local contributions (US EPA, 2009a; sections 9.2.3.3 and 9.2.3.4), suggesting 

the potential for higher levels of PM-induced visibility impairment in urban areas; 2) the 

existence of numerous urban visibility protection programs and goals demonstrating that urban 

VAQ is noticed and considered an important value to urban residents (US EPA, 2009a; section 

9.2.4); and 3) the existence of large urban populations means that potentially more people are 

routinely affected by poor VAQ than in rural areas. These features of urban areas have led EPA 

staff to conclude that urban dwellers represent a susceptible population group for adverse PMrelated effects on visibility. However, this conclusion is not meant to imply that there are not 

other susceptible populations or individuals living in other non-urban and non-Class I areas that 

are currently adversely impacted by ambient PM-related visibility conditions. Unfortunately, 

visibility preferences and PM levels in these areas have not been well characterized. Although 

this visibility assessment focuses only on selected urban areas, a new secondary PM standard would 

apply to all non-Class I areas of the country.

On October 6-8, 2008 the EPA sponsored an urban visibility workshop in Denver, 

Colorado to identify and discuss methods and materials that could be used in “next step” projects 

to develop additional information about people’s preferences for reducing existing impairment of 

urban visibility, and about the value of improving urban visibility. Invited individuals came 

from a broad array of relevant technical and policy backgrounds, including visual air quality 

science, sociology, psychology, survey research methods, economics, and EPA’s process of 

setting NAAQS. The 23 people who attended the workshop (including one via teleconference 

line) came from EPA, the National Oceanic and Atmospheric Administration (NOAA), National 

Park Service, academia, regional and state air pollution planning agencies, and consulting 
1-14 

firms.10 The information discussed at this Workshop was useful in informing subsequent steps in 

the process. 

1.4.2 Selection of Alternative Scenarios for First Draft Assessments 

In designing the quantitative assessments to include in the first draft UFVA, EPA staff 

developed a planning document outlining the initial design for the PM NAAQS visibility 

assessment - Particulate Matter National Ambient Air Quality Standards: Scope and Methods 

Plan for Urban Visibility Impact Assessment, henceforth Scope and Methods Plan (US EPA, 

2009b). This planning document was released for CASAC consultation and public review in 

February 2009. Based on consideration of CASAC and public comments on the Scope and 

Methods Plan, along with ongoing review of the latest PM-related literature, several aspects of 

the original scope of the urban visibility conditions assessment, as depicted in Figure 1-1 of 

section 1.3 of the Scope and Methods document (US EPA, 2009b), were modified in the first 

draft UFVA (US EPA, 2009c). Taking into account the nature of urban versus more remote area 

PM composition, and input received at the April 2, 2009 CASAC meeting, EPA staff concluded 

that it was unnecessary to develop a new urban-optimized algorithm at this time and that it 

remained appropriate in the context of this assessment to use the original IMPROVE algorithm 

to relate urban PM to local haze (PM light extinction). One of the primary reasons for initially 

considering an urban-optimized algorithm was a concern that the organic components of PM in 

urban areas, being generally nearer their emission sources, would have a lower ratio to the 

measured organic carbon mass than the ratio of organic component mass to measured organic 

carbon mass currently used for the more aged PM organic components found in remote areas. 

As described below in chapter 3, this concern has been addressed by using the SANDWICH 

mass balance approach to estimate the PM organic component mass, which negates the need to 

estimate organic component mass from measured organic carbon mass. 

With regard to the urban visual air quality preference assessment described in the Scope 

and Methods document (US EPA, 2009b, section 1.3), more significant modifications occurred. 

EPA staff decided to conduct a reanalysis of the urban visibility preference studies available at 

the time of the 2006 PM NAAQS review, rather than conduct new public preference studies, as it 

has become apparent that the results of these studies would be unlikely to be completed in time 

to inform this review. Recognition that the initial plans described in the Scope and Methods 

document were possibly overly ambitious was also shared by members of CASAC (see 

individual member comments; Samet, 2009a). The analysis, therefore, relied on existing, rather 

than new, urban visibility preference studies and was designed to explore the similarities and 

 

10 To view the complete report from the October 2008 urban visibility workshop, see: 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.h tm
1-15 

differences (comparability) among these studies. Information drawn from these results informed 

the selection of VAQ CPLs (described in chapter 2 below) to be used in subsequent impact 

assessments. Further, information presented during the public comment phase of the April 2, 

2009 CASAC meeting and later provided to EPA staff, led to the inclusion of a recent study by 

Smith and Howell (2009) for Washington, DC in the reanalysis. 

1.4.3 Selection of Alternative Scenarios for Second Draft Assessments 

The first draft UFVA was reviewed at an October 2009 CASAC meeting, and a CASAC 

letter providing its advice and recommendations was submitted to the Administrator in 

November 2009 (Samet, 2009b). In its letter, the CASAC indicated support for EPA staff’s 

approach to evaluating the nature and degree of PM-related visibility impairment, including 

EPA’s focus on non-Class I areas, including in particular, urban areas as an “effective 

complement” to the Regional Haze Rule. In this regard, CASAC expressed support for 

consideration of a new PM light extinction indicator, a one hour averaging time, and for the 

range of selected candidate protection levels. 

 Indicator: PM10 Light Extinction 

There are a number of different ways to measure ambient PM: particle counts, surface 

area, volume, mass concentrations, and concentration of components. Each of these different 

characteristic of ambient PM can be important in the context of different effects. For example, 

particle count may be important from the perspective of cloud formation or to characterize the 

abundance of ultrafine PM, which is of interest for health effects. In a similar way PM light 

extinction measures the characteristic of ambient PM most relevant and directly related to the 

effect of PM visibility impairment. Thus, as described in the Scope and Methods document (US 

EPA, 2009b) and first and second drafts of the UFVA, EPA staff has continued to focus 

assessments in this final document in terms of ambient PM10 light extinction as the indicator for 

PM visibility impairment, instead of the traditional PM2.5 mass concentration. Unlike the current 

FRM measurement method for PM mass concentration, which generally changes the 

composition and size of the particles by driving off most of the water, direct measurement of 

ambient PM light extinction captures the PM-induced visibility impairment of the particles as 

they exist in the atmosphere. PM light extinction, like conventional PM mass concentration, is a 

measurable physical characteristic of atmospheric PM. PM light extinction can also be 

calculated using a simple algorithm such as the IMPROVE algorithm11 

Section 109 (b) (2) of the CAA states that “Any national secondary ambient air quality 

standard prescribed under subsection (a) of this section shall specify a level of air quality the 

 

11 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.) 
1-16 

attainment and maintenance of which ... is requisite to protect the public welfare from any 

known or anticipated adverse effects associated with the presence of such air pollutant in the 

ambient air....” (emphasis added). In addition, section 108 (a) (2) states that the air quality 

criteria “for an air pollutant shall accurately reflect the latest scientific knowledge useful in 

indicating the kind and extent of all identifiable effects on public health or welfare which may be 

expected from the presence of such pollutant in the ambient air, in varying quantities. The 

criteria ... shall include information on (A) those variable factors (including atmospheric 

conditions) which of themselves or in combination with other factors may alter the effects on 

public health or welfare of such air pollutant;...” (emphasis added). Thus, EPA staff believes 

that the visibility effects of PM important to the public welfare are precisely the visibility effects 

of PM occurring in the ambient air, which necessarily entails association with ambient 

atmospheric conditions that affect the nature or magnitude of the PM visibility effect. These 

ambient conditions lead to constant changes in the size and composition of particles as these 

particles come in contact with other pollutants or natural components, become oxidized/age as 

they are transported great distances, and shrink or grow in the absence or presence of water 

vapor, or other atmospheric gases. The combined effect of all these interactions of ambient PM 

with real time atmospheric conditions and chemistry on the public welfare effect of visibility 

impairment depends on factors other than dry PM mass concentration alone. Use of PM10 light 

extinction as the indicator for a secondary PM NAAQS is thus a more direct measure of the 

relationship between ambient PM and the public welfare effect of visibility impairment than any 

dry PM mass concentration (either PM2.5 or any other dry mass fraction). 

 Averaging times: Daylight Daily Max. 1 Hour or All Daylight Hours 

It is necessary to also identify an averaging time to apply along with the CPLs in the 

assessments described in chapters 3 and 4. Because the nature of visibility impairment and its 

impact on the public welfare is sufficiently different and less well understood at night, this 

assessment only considers visibility conditions that occur during daylight hours. Though not 

directly supported by preference or other studies, EPA staff believes that a short averaging time 

(e.g. an hour) may be more appropriate than longer time periods (e.g. multiple hours) since VAQ 

impacts are instantaneously perceived. This is also consistent with staff’s belief that most 

individuals in an urban setting experience urban VAQ in relatively short-term incidental and 

intermittent periods when they have the opportunity to be outdoors (e.g. during commutes to 

work, school, shopping, etc.). Since this fraction of the public may experience poor VAQ during 

a relatively small time period and not have the opportunity to see it improve later during the 

same day, it seems appropriate to EPA staff to consider assessing the current and projected 

conditions in chapters 3 and 4 by comparing the 1-hour daily maximum light extinction to each 
1-17 

of the three CPLs supported by the preference studies. There is uncertainty associated with 

predicting the duration of the effect associated with such brief periods of exposures, i.e., it is not 

known how long the person remembers the poor VAQ conditions once he/she goes indoors and 

is removed from the sight. 

Alternately, a complementary fraction of the public may have multiple or continuing 

opportunities to experience visibility throughout the day. People in this situation can experience 

a variety of conditions ranging from improvement, maintenance, or diminished VAQ throughout 

the day. For them, a day with several hours that exceed acceptable VAQ levels may represents a 

greater impact on their wellbeing than on a day with only one such hour. To assess impacts 

more related to this portion of the population, in which the degree of impact depends upon the 

conditions present across multiple hours of exposure, EPA staff has also considered all daylight 

hours which have light extinction levels beyond the three CPLs, as well as the 1-hour daily 

maximum light extinction in the assessments described in chapters 3 and 4. 

 Level: Candidate Protection Levels (CPLs) 

In order to identify a range of light extinction levels associated with acceptable VAQ to 

compare to current and projected conditions in the assessment in chapters 3 and 4 of this 

document, CPLs have been selected in a range from 20 dv to 30 dv (74 Mm-1 to 201 Mm-1) based 

on the composite results and the effective range of 50th percentile acceptability across the four 

urban preference study areas shown in Figure 2-16. A midpoint of 25 dv (122 Mm-1) was also 

selected for use in the assessment. These three values provide a low, middle, and high set of 

light extinction conditions that are used in subsequent sections of the UFVA to define daylight 

hours with urban haze conditions that have been judged unacceptable by the participants of these 

preference studies. As discussed in greater detail in section 1.2 above, PM light extinction is 

taken to be light extinction minus the Rayleigh scatter (i.e. light scattering by atmospheric gases 

is about 10 Mm-1) and NO2 contribution (assumed to be negligible), so the PM light extinction 

levels that correspond to low, middle and high CPLs are about 64 Mm-1, 112 Mm-1 and 

191 Mm-1, respectively. 

 Forms: Percentiles and Relative Humidity Constraints 

In considering an appropriate range of forms to consider in the analyses of alternative PM 

light extinction visibility standards analyzed in chapter 4 of this final UFVA, staff considered 

what frequency of conditions at or below the CPLs should be considered acceptable. Again, 

none of the preference studies provided insight into this aspect of acceptability. Because the 

nature of the public welfare effect is one of aesthetics and/or on feelings of wellbeing and not 

directly related to a physical health outcome, EPA staff believes that it is not necessary to 
1-18 

eliminate all such exposures and that some number of hours/days with poor VAQ can reasonably 

be tolerated. In the first draft UFVA, staff selected the 90th and 95th percentiles to assess. In the 

CASAC letter following the review of the first draft UFVA, CASAC recommended that other 

percentiles be considered, up to and including the 98th percentile used for the current 24-hour primary 

and secondary standards. EPA staff has therefore considered the 90th, 95th and 98th percentiles per 

year in the second and final iterations of this document. Due to inter-annual variability in 

meteorology and other circumstances that affect air quality, EPA staff is recommending using a 

three year average form of the standard for purposes of consistency and stability, as is the current 

24-hour primary PM2.5 standard. By considering all of the combinations of the two hourly forms 

(i.e. each daylight hour and daylight 1-hour daily maximum), the three CPLs and the three 

frequencies, a total of 18 separate alternative secondary PM NAAQS scenarios were generated 

for use in the assessments described below in chapters 3 and 4 (See table 4-1). An additional 

CASAC recommendation, that the relative humidity (RH) limit be lowered from 95% to 90% 

and used as a screen (i.e., hours above it should be discarded) rather than as a cap, to more 

clearly exclude weather events like fog or precipitation and to minimize effects of measurement 

error and spatial variability, was also incorporated into the second and final iterations of this 

document. 

1.5 ORGANIZATION OF DOCUMENT 

The remainder of this document is organized as follows: Chapter 2 includes an analysis 

of the urban visibility preference studies with a discussion of similarities and differences 

regarding the approaches and methods used and results obtained for each study. This chapter 

also includes a summary discussion of the results of a composite assessment of the combined 

results from the four urban areas (Denver, Phoenix, British Columbia, and Washington, DC, an 

accompanying logit (statistical) analysis, and use of these results in the selection of the 

alternative levels evaluated in the remainder of the assessment. The complete description of the 

logit analyses is found in Appendix J. Chapter 3 describes the analytical approach, methods, and 

data used in conducting the assessment of recent urban visibility conditions, both in terms of PM 

mass concentration and PM light extinction for the set of urban case studies included in this 

analysis. Selected results are presented in chapter 3, with additional results found in the 

Appendices. Chapter 4 presents estimates of PM mass concentration and PM10 light extinction 

conditions generated for the urban case studies for two alternative PM2.5 mass concentration 

levels and for the three light extinction CPLs. Additional information regarding approaches, 

results, method validation studies and uncertainty assessments for both chapters 3 and 4 are 

presented in Appendices A-J). 
2-1

2 URBAN VISIBILITY PREFERENCE STUDIES 

The purpose of this chapter is to describe the reanalysis of available urban visibility 

preference studies conducted by EPA staff with contractor support. The reanalysis covered the 

three completed urban visibility preference survey studies plus a pair of smaller focus studies 

designed to explore and further develop urban visibility survey instruments. The three 

completed survey studies (all in the west) included Denver, Colorado (Ely et al., 1991), one in 

the lower Fraser River valley near Vancouver, British Columbia (BC), Canada (Pryor, 1996), 

and one in Phoenix, Arizona (BBC Research & Consulting, 2003). The first pilot focus group 

study was conducted in Washington, DC on behalf of EPA to inform the 2006 PM NAAQS 

review (Abt Associates Inc., 2001). In response to an EPA request for public comment on the 

Scope and Methods Plan (74 FR 11580, March 18, 2009) for the current review, Dr. Anne Smith 

provided comments (Smith, 2009) about the results of a new Washington, DC focus group study 

that had been conducted using methods and approaches similar to the method and approach 

employed in the EPA pilot study (Smith and Howell, 2009). When taken together, these studies 

from the four different urban areas included a total of 852 individuals, with each individual 

responding to a series of questions answered while viewing a set of images of various urban 

VAQ conditions. The apparent similarity in the methods used across the studies made it appear 

initially that the studies were comparable. 

However, in order to ensure that our basis for selecting an appropriate range of CPLs was 

sound, we, along with contractor support, undertook a detailed reanalysis to determine the 

robustness of the survey study results, the appropriateness of comparing each study’s results to 

the others, and the key uncertainties relevant to data interpretation. This reanalysis included a 

statistical analysis using a logit regression model to assess the comparability of different datasets. 

Limited discussion of logit model results occurs in the body of this chapter when pertinent to 

informing staff judgments regarding comparability of study results. A detailed description of the 

logit assessment is provided in the contractor memo included as Appendix J of this document. 

The following sections (sections 2.1 to 2.5) examine in detail the study methods used and results 

obtained from each of the available studies. 
2-2

2.1 METHODS USED IN VISIBILITY PREFERENCE STUDIES 

In all but one1

 of the visibility preference studies assessed in this document, participants 

were shown a series of different VAQ conditions projected on a large screen using a slide 

projector. In the earliest two studies (the Denver and lower Frazer River Valley British 

Columbia studies) the range of VAQ conditions were presented by projecting photographs 

(slides) of actual VAQ conditions. The photographs were taken on different days from the same 

location, and presented the same scene. Photographs were selected to avoid depicting significant 

weather events (e.g., rain, snow, or fog), and where measured extinction data were available 

from the time the photograph was taken. 

The Phoenix study and the Washington, DC projects used computer generated 

photographic-quality images to present different VAQ conditions. Using an original nearpristine base photograph, additional images representing a range of VAQ conditions were 

generated using the WinHaze software program, which is based on a technique described in 

Molenar et al. (1994). The Phoenix study and the 2001 Washington, DC project projected slides 

of digital images prepared by WinHaze. The 2009 Washington, DC project presented images 

directly from the desktop version of WinHaze using either a liquid crystal display (LCD) 

projector or a computer monitor. 

WinHaze analysis synthetically superimposes a uniform haze on a digitized, actual 

photograph. The WinHaze computer algorithm calculates how a given extinction level would 

impair the appearance of each individual portion of the photograph. A major advantage of 

presenting WinHaze-generated images is that they provide viewers depictions of alternative 

VAQ levels, with each image containing exactly the same scene, with identical light angle, time 

of day properties, weather conditions, and specific scene content details (e.g., the amount of 

traffic in a intersection). Additional details about WinHaze, and a discussion of the applicability 

of WinHaze images for regulatory purposes, is in the 2004 PM Criteria Document (U.S. EPA, 

2004). The desktop version of WinHaze is available online (Air Resources Specialists, 2003). 

The first urban visibility preference study (Denver, CO; Ely et al., 1991) developed the 

basic survey method used in all the subsequent studies. Although there are variations in specific 

details in each study, all the studies use a similar overall approach (key variations are discussed 

in the section on each study later in this chapter). This approach consisted of conducting a series 

of group interview sessions, where the participants were shown a set of photographs or images of 

alternative VAQ conditions and asked a series of questions. 

 

1

 Smith and Howell (2009) used digital projection technology not used by the other studies to present the 

series of VAQ conditions. Some of the participants in the Smith and Howell study were shown images using a LCD 

projector connected to a laptop computer. In other sessions, participants in the Smith and Howell study were shown 

images on a computer monitor connected to the computer.
2-3

 The group interview sessions were conducted multiple times with different participants. 

Ideally the participants will be a representative sample of the residents of the metropolitan area. 

While all studies agree that this is the preferred approach, due to the high cost of organizing and 

conducting a series of in-person group interviews with a large, statistically representative sample, 

only the Phoenix study was able to fully meet this objective. During the group interview 

sessions, the participants were instructed to consider whether the VAQ in each photograph or 

image would meet an urban visibility standard, according to their own preferences and 

considering three factors: 

1. The standard would be for their own urban area, not a pristine national park area 

where the standards might be stricter. 

2. The level of an urban visibility standard violation should be set at a VAQ level 

considered to be unreasonable, objectionable, and unacceptable visually. 

3. Judgments of standards violations should be based on visibility only, not on 

health effects. 

The photographs (images) were not shown in order of ascending or descending VAQ 

conditions; the VAQ conditions were shown in a randomized order (with the same order used in 

each group interview session). In order to check on the consistency of each individual’s 

answers, the full set of photographs (images) shown during the group interview included 

duplicates with the identical VAQ conditions. 

The participants were initially given a set of “warm up” exercises to familiarize them with 

how the scene in the photograph or image appears under different VAQ conditions. The 

participants next were shown 25 randomly ordered photographs (images), and asked to rate each 

one based on a scale of 1 (poor) to 7 (excellent). They were then shown the same photographs or 

images again (in the same order), and asked to judge whether each of the photographs (images) 

would violate what they would consider to be an appropriate urban visibility standard (i.e., 

whether the level of impairment was “acceptable” or “unacceptable”). While the studies all 

asked the basic question, “What level of visibility degradation is acceptable?”, the term 

“acceptable” was not defined, so that each person’s response was based on his/her own values 

and preferences for VAQ. 

2.2 DENVER, COLORADO 

The Denver urban visibility preference study (Ely et al., 1991) was conducted on behalf 

of the Colorado Department of Public Health and Environment (CDPHE). The study consisted 

of a series of focus group sessions conducted in 1989 with participants from 16 civic 
2-4

associations, community groups, and employees of state and local government organizations.2

 

The participants were not selected to be a fully representative sample of the Denver metropolitan 

population but were instead selected to take advantage of previously scheduled meetings. 

During the 16 focus group sessions, a total of 214 individuals were asked to rate 

photographs of varying visibility conditions in Denver. The photographs were taken November 

1987 through January 1988 by a camera in Thornton, Colorado. Thornton is suburb of Denver, 

located approximately six miles north of downtown Denver. The photographs were taken as part 

of a CDPHE study of Denver’s air quality. The scene in the photographs was toward the south 

from Thornton and included a broad view of downtown Denver and the mountains to the south. 

Each group was shown one of two sets of 20 randomly ordered unique photographs (13 of the 

sessions included 5 duplicate slides, for a total of 25 photographs, to evaluate consistency of 

responses). The two sets of different slides were used to investigate whether the responses 

between the two sets of photographs were different (no differences were found). Approximately 

100 participants viewed each photograph. Projected color slides were used to present the 

photographs to focus group participants, and were projected on a large screen 

The VAQ conditions in each Denver photograph were recorded when the photograph was 

taken and measured by a transmissometer yielding hourly average light extinction, bext. The 

transmissometer was located in downtown Denver, approximately eight miles from the camera 

and in the middle of the camera’s view path. Ely et al. (1991) provide the time of day and 

measured extinction level for each photograph. The extinction levels presented in the Denver 

photographs ranged from 30 to 596 Mm-1. This corresponds to 11dv to 41dv, approximating the 

10th to 90th percentile of wintertime visibility conditions in Denver in the late 1980s. 

The participants first rated the VAQ in each photograph on a 1 to 7 scale, and 

subsequently were asked if each photograph would violate an urban visibility standard. The 

individual’s rating on the 1 to 7 scale and whether the photograph violated a visibility standard 

were highly correlated (Pearson correlation coefficient greater than 80%). 

The percent of participants who found a photograph acceptable to them (i.e., would meet 

an appropriate urban visibility standard) was calculated for each photograph. Figure 2-1 shows 

the results of the Denver participants’ responses, with VAQ measured in deciviews. 

 

2

 No preference data were collected at a 17th focus group session due to a slide projector malfunction. 
2-5

Figure 2-1. Percent of Denver Participants Who Considered VAQ in Each 

Photograph “Acceptable” 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating 

"acceptable"

% acceptable Denver standard

Ely et al. (1991) introduce a “50% acceptability” criteria analysis of the Denver 

preference study results. The 50% acceptability criteria is designed to identify the VAQ level 

that best divides the photographs into two groups: those with a VAQ rated as acceptable by the 

majority of the participants, and those rated not acceptable by the majority of participants. While 

no single VAQ level creates a perfect separation between the two groups, the CDPHE identified 

a VAQ of 20.3 dv as the point that best separates the Denver study responses into “acceptable” 

and “not acceptable” groups. Based in part on the findings of the Denver visibility preference 

study, the CDPHE established a Denver visibility standard at bext = 76 Mm-1 (dv = 20.3). 

Using 20.3 dv as the 50% acceptability criteria led to six photographs being 

inconsistently rated by the majority of the viewers. A photograph was inconsistently rated for 

two possible reasons; either the photograph’s VAQ was at least 1 dv better than the Denver 

standard (i.e., dv < 19.3) but was judged to be “unacceptable” by a majority of the participants 

rating that photograph, or the VAQ was at least 1 dv worse than the standard (> 21.3 dv) but 

found to be acceptable by the majority of the participants. This definition of inconsistent rating 

helps evaluate the robustness of the study results to support the selection of the Denver urban 

visibility standard at 76 Mm-1 (20.3 dv) by identifying photographs with VAQ a minimum of 1 

dv above or below the standard and ignoring “near misses” involving photographs within 1 dv of 

the standard. A change of 1 or 2 dv in uniform haze under many viewing conditions will be seen 
2-6

as a small but noticeable change in the appearance of a scene, regardless of the initial haze 

condition (U.S. EPA, 2004). 

Table 2-1 presents information about the six photographs that were inconsistently rated. 

All six of the inconsistently rated photographs were taken at 9:00 a.m. The five inconsistently 

rated photographs with a VAQ better than the Denver standard have a VAQ at least 2 dv below 

the standard. The VAQ in the only inconsistently rated photograph with air quality worse than 

the standard (Photograph #6) is 1.1 dv above the standard. The study used 18 photographs from 

9:00 a.m., so a third of the 9:00 a.m. photographs were inconsistently rated. Conversely, none of 

the 32 photographs taken at noon or 3:00 p.m. were inconsistently rated. 

Table 2-1. VAQ of Denver Photos Substantively Misclassified by Majority of Participants 

Photograph # 

VAQ in photograph 

in extinction 

(Mm-1) 

VAQ in 

photograph (dv)

% of participants 

who rated the photo 

“acceptable” 

Time of day of 

photograph 

14 44 13.8 43% 9:00 a.m. 

18 54 16.9 43% 9:00 a.m. 

19 54 16.9 31% 9:00 a.m. 

20 55 17.0 42% 9:00 a.m. 

24 60 17.9 13% 9:00 a.m. 

36 85 21.4 72% 9:00 a.m. 

Figure 2-2 shows the same data results about percent of participants who rated each 

photograph acceptable as in Figure 2-1, but with the time of day of each photograph indicated by 

different colors. The time of day colors clearly indicate how inconsistently participants rated 

some of the 9:00 a.m. photographs. 

Eliminating the 9:00 a.m. photographs creates a “hole” in the range of remaining 

photographs; there are no photographs with a VAQ between 17.7 dv and 20.3 dv. As seen in 

Figure 2-3, this is a critical range in evaluating the responses. All of the photographs with a 

VAQ equal to or better (i.e., a lower dv value) than 17.7 dv are rated acceptable by the majority 

of the participants, and all photographs with a VAQ at or above 20.3 dv are rated not acceptable. 

After eliminating the 9:00 a.m. photographs, any VAQ level between 17.7dv and 20.3 dv would 

completely divide the photographs into two groups with no inconsistent ratings. 
2-7

Figure 2-2. Photograph Time of Day Information 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating 

"acceptable"

9:00 AM 12:00 PM 3:00 PM Denver standard

A modestly broader range of VAQ conditions provides an even more unambiguous 

interpretation of the Denver study results. Every photograph with a VAQ of 17.7 dv or lower 

was rated acceptable by 89% or more of participants, and every photograph with a VAQ of 24.6 

or higher was rated not acceptable by 84% or more of the participants. The 17.7 dv to 24.6 dv 

range separating the results is shown in Figure 2-3, which also eliminates the 9:00 a.m. results. 
2-8

Figure 2-3. Denver Photograph Time of Day Results (9:00 a.m. Photographs 

Eliminated), with the Broader Range (17.7 dv and 24.6 dv) of the 

50% Acceptability Criteria Shown 

Denver Preference Results Showing Broader Range 

Separating Unambigious Ratings

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% Participants Rating 

"Acceptable"

12:00 PM 3:00 PM Denver Standard Lower Bound Upper Bound

2.3 VANCOUVER, BRITISH COLUMBIA, CANADA 

The BC urban visibility preference study (Pryor, 1996) was conducted on behalf of the 

BC Ministry of Environment following the methods used in the Denver study. Participants were 

students at the University of British Columbia, who were in one of four focus group sessions 

with between 7 and 95 participants. A total of 180 participants completed the surveys (29 did 

not complete the survey). 

The BC study used photographs (projected as slides) depicting various VAQ conditions 

in two cities (Chilliwack and Abbotsford) in the lower Fraser River valley in southwestern BC. 

Abbotsford is located approximately 75 miles east of Vancouver, BC, and had a 2006 population 

of 159,000 (Statistics Canada, 2009a). Abbotsford has a diverse and successful economy, with 

approximately 25% of the labor force working in the Vancouver metropolitan area. Chilliwack 

is adjacent to Abbotsford to the east. Both cities have experienced rapid population growth, 

growing faster than the Vancouver metropolitan area, and are considered suburbs (or exurbs) of 

Vancouver. 

The survey was conducted at the University of British Columbia (UBC) in 1994. The 

participants were 206 undergraduate and graduate students enrolled in classes in UBC’s 
2-9

Department of Geography. Information about student demographics and where they lived prior 

to enrolling at UBC (which potentially influences their knowledge of, and preferences for, 

Vancouver area visibility) is not available. 

The BC survey showed 20 unique photographs to the participants in random order. Ten 

photographs were from Chilliwack, and 10 were from Abbotsford. The Chilliwack photographs 

were taken at the Chilliwack Hospital, and the scene includes a complex foreground with 

downtown buildings, with mountains in the background up to 40 miles away. Figure 2-4 is a 

composite of two of the Chilliwack photographs used in the preference study, showing the scene 

with a good visibility day (14.1 dv) in the middle and a significantly impaired day (34 dv) around 

the border (Jacques Whitford AXYS, 2007). The Abbotsford photographs were taken at the 

Abbotsford Airport. The Abbotsford scene includes fewer man-made objects in the foreground 

and is primarily a more rural scene with the mountains in the background up to 36 miles away. 

Figure 2-4. Composite Chilliwack, BC Photograph Shows VAQ of 14.1 dv and 34 dv

The photographs were taken in July and August 1993 as part of a VAQ and fine 

particulate monitoring project sponsored by the BC Ministry of Environment, Lands and Parks 

(REVEAL, the Regional Visibility Experimental Assessment in the Lower Fraser Valley). All of 

the photographs were taken at either 12:00 p.m. or 3:00 p.m. VAQ data were available for each 

photograph from visibility monitors near the location of each camera. The types of VAQ 

measurement data available from the two locations were not identical. The Chilliwack location 
2-10

measurement data available from the two locations were not identical. The Chilliwack location 

used both an open-chamber nephelometer and a long path transmissometer and collected hourly 

average data on both aerosol light scattering (bsp) and total extinction (bext), respectively. The 

visibility monitoring at the Abbotsford location had only a nephelometer and collected only bsp

data. 

As explained in section 1.3, total light extinction is the sum of scattering by gases (bsg) 

and particles (bsp) plus light absorption by gases (bag) and particles (bap). In order to present the 

preference results from the BC study in comparable terms, bext for the Abbotsford photographs is 

estimated by assuming that the average of the ratios of PM light extinction (i.e., bap + bsp) to PM 

light scattering (bsp) for all ten of the Chilliwack photographs can be multiplied by the 

Abbotsford nephelometer determined bsp values corresponding to each of its photographs to 

estimate its PM light extinction value. By assuming that absorption by gases (bag) is zero, total 

light extinction is equal to the PM light extinction (i.e., bap + bsp) plus particle scattering by gases 

(i.e., bsg that is approximately equal to 10 Mm-1). Table 2-2 presents the data from the 

photographs used in the BC study, including the estimated bext for the Abbotsford photographs. 

There are two caveats to be noted about the extinction data for the photographs reported 

in Pryor, 1996. First, in Table 2 of the original article, two of the Abbotsford photographs are 

listed with the same date and time (12:00 p.m., 7/26/1993). There is no information provided for 

a 3:00 p.m., 7/26/1993 Abbotsford photograph, although there is a Chilliwack photograph from 

that time. The preference and VAQ data are presumed to be correct for both photographs and 

one of the two identical date/time labels is assumed to be a typographic error. The second caveat 

is that bsp levels from the same date and time can differ substantially between Abbotsford and 

Chilliwack, and the relative levels can change rapidly, even though the two cities are only 25 

miles apart. For example, at 12:00 p.m. on 8/19/1993, the bsp level in Chilliwack was about onethird of the Abbotsford bsp level. By 3:00 p.m. the situation was reversed, with the Chilliwack 

bsp level 50% higher than Abbotsford. In those three hours the Chilliwack bsp level had more 

than doubled (from 46 Mm-1 to 105 Mm-1), and the Abbotsford level had fallen by over half 

(from 145 Mm-1 to 67 Mm-1). Such substantial changes in measured bsp levels occurring across a 

relatively short period of time and short distance, may reflect an inherent uncertainty introduced 

by using a single measure of light extinction from a portion of visual scene (where the 

nephelometer or transmissometer was operating) to assess visibility conditions throughout an 

actual photographs of a complex scene. Spatial and temporal non-uniformity of visibility 

conditions within a scene are an atmospheric condition known to occur on some days, and may 

contribute to the variability in participant responses in preference studies utilizing actual 

photographs. 
2-11

Table 2-2. Summary of Photographs Used in British Columbia Study 

Date Time bsp bext

Ratio 

(bext-bsg)/bsp

Estimated 

bext Deciview 

Chilliwack 

7/26/93 12:00 p.m. 86 128 1.372 NA 25.49 

7/26/93 3:00 p.m. 67 112 1.522 NA 24.16 

7/27/93 12:00 p.m. 63 105 1.508 NA 23.51 

7/27/93 3:00 p.m. 119 185 1.471 NA 29.18 

8/2/93 12:00 p.m. 18 37 1.5 NA 13.08 

8/2/93 3:00 p.m. 20 36 1.3 NA 12.81 

8/5/93 12:00 p.m. 45 70 1.333 NA 19.46 

8/5/93 3:00 p.m. 51 96 1.686 NA 22.62 

8/19/93 12:00 p.m. 46 81 1.543 NA 20.92 

8/19/93 3:00 p.m. 105 170 1.524 NA 28.33 

Average 62 102 1.476 21.96 

Abbotsford 

7/26/93 12:00 p.m. 39 NA NA 68 19.17 

7/26/93 12:00 p.m. 82 NA NA 131 25.73 

7/27/93 12:00 p.m. 104 NA NA 205 30.20 

7/27/93 3:00 p.m. 132 NA NA 164 27.97 

8/2/93 12:00 p.m. 24 NA NA 45 15.04 

8/2/93 3:00 p.m. 25 NA NA 47 15.48 

8/5/93 12:00 p.m. 62 NA NA 121 24.93 

8/5/93 3:00 p.m. 75 NA NA 102 23.22 

8/19/93 12:00 p.m. 67 NA NA 224 31.09 

8/19/93 3:00 p.m. 145 NA NA 109 23.89 

Average 76 122 23.67 

Figure 2-5 presents the results of the BC study. The division corresponding to the 

Denver “50% acceptable” criteria occurs between 22.6 dv and 23.2 dv. All of the photographs 

with a VAQ better than 22.6 dv were rated acceptable by the majority of the participants with 

one exception (47% of the participants judged the 19.2 dv photograph to be acceptable). All 

photographs with a VAQ better than 19.2 dv were rated acceptable by over 90% of the 
2-12

participants. All photographs with a VAQ worse than 22.6 dv were rated not acceptable by the 

majority of the participants, and all photographs with a VAQ worse than 28.3 dv were rated not 

acceptable by over 90% of the participants. 

Figure 2-5 also suggests that there may be some difference between the preferences 

expressed for the Chilliwack scene and those for the Abbotsford scene. All photographs were 

rated by the same individuals (students at UBC), but the summary of the responses indicate that 

the participants may have rated as acceptable a worse level of impaired VAQ impairment (e.g., 

higher dv levels) in photographs showing more of a downtown area (Chilliwack) than in less 

congested scenes (Abbotsford). The strongest evidence for this hypothesis, however, is the 

preference for a single photograph (the 19.0 dv photograph from Abbotsford, rated as acceptable 

by 47%), previously identified as an outlier observation. 

Figure 2-5. Percent of BC Participants Who Consider VAQ in Each Photograph 

“Acceptable” 

The BC Ministry of the Environment is considering the BC urban visibility preference 

study as part of establishing urban and wilderness visibility goals in BC. 
2-13

2.4 PHOENIX, ARIZONA 

The Phoenix urban visibility preference study (BBC Research & Consulting, 2003), 

which was conducted on behalf of the Arizona Department of Environmental Quality, used 

group interviews based on the methods used in the Denver study, with two major exceptions: (1) 

the focus group participants were selected as a representative sample of the Phoenix area 

population, and (2) the pictures presented in the focus groups were computer-generated images 

to depict specific uniform haze conditions. 

The Phoenix study included 385 participants in 27 separate focus group sessions. 

Participants were recruited using random digit dialing to obtain a sample group designed to be 

demographically representative of the larger Phoenix population. During July 2002, group 

interview sessions took place at six neighborhood locations throughout the metropolitan area to 

improve the participation rate. Participants received $50 as an inducement to participate. 

Three sessions were held in Spanish in one region of the city with a large Hispanic 

population (25%), although the final overall participation of native Spanish speakers (18%) in 

the study was below the targeted level. The age distribution of the participants corresponded 

reasonably well to the overall age distribution in the 2000 U.S. Census for the Phoenix area 

(BBC Research & Consulting, 2003). Participants slightly over-represented the middle-income 

range ($50,000 to $74,999), compared with 2000 Census data, and slightly under-represented 

very low-income ranges (under $24,999). The distribution of participant education levels was 

fairly consistent with the education distribution in the 2000 Census. 

Photographic-quality slides of the images were developed using the WinHaze software 

(Molenar et al., 1994). The scene used in the Phoenix study images was taken at a water 

treatment plant. The view is toward the southwest, including downtown Phoenix, with the Sierra 

Estrella Mountains in the background at a distance of 25 miles. Figure 2-6 shows the image with 

the best VAQ (15 dv). 
2-14

Figure 2-6 Reproduction of Image with the Best VAQ (15 dv) Used in the Phoenix Study

The study used a total of 21 unique WinHaze images. Four of the 21 unique images were 

randomly selected and used twice to evaluate consistency so that participants viewed a total of 

25 images. The 25 images were randomly ordered, with all participants viewing the images in 

the same order. The WinHaze images used in the Phoenix study do not include layered haze, a 

frequent and widely recognized form of visibility impairment in the Phoenix area. 

The VAQ levels in the 21 unique images ranged from 15 dv to 35 dv (the extinction 

coefficient bext ranged from 45 Mm-1 to 330 Mm-1). As in the Denver study, participants first 

individually rated the randomly shown slides on the same VAQ scale of 1 to 7. Participants were 

instructed to rate the photographs solely on visibility and to not base their decisions on either 

health concerns or what it would cost to have better visibility. Next, the participants individually 

rated the randomly ordered slides as “acceptable” or “not acceptable,” defined as whether the 

visibility in the slide is unreasonable or objectionable. 

Figure 2-7 presents the percent acceptability results from the Phoenix study. The 

combination of the use of WinHaze images and the larger number of participants than in the 

Denver study may account for the “smoother” backwards S-shaped pattern of preferences. 
2-15

Figure 2-7. Percent of Phoenix Participants who Consider VAQ in Each Image 

“Acceptable” 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating "acceptable"

Ninety percent or more of the participants rated a VAQ of 20 dv or better as acceptable, and 70% 

rated a VAQ of 22 dv or better as acceptable. The “50% acceptable criteria” was met at 

approximately 24.3 dv (with 51.3% of the participants rating that image as acceptable). The 

percent acceptability declines rapidly as VAQ worsens; only 27% of the participants rated a 

26 DV image as acceptable, and fewer than 10% rated a 29 dv image as acceptable. 

The Phoenix urban visibility study formed the basis of the decision of the Phoenix 

Visibility Index Oversight Committee for a visibility index for the Phoenix metropolitan area 

(Arizona Department of Environmental Quality, 2003). The Phoenix Visibility Index establishes 

an indexed system with 5 categories of visibility conditions, ranging from “Excellent” (14 dv or 

less, which was a better VAQ than any of the images used in the Phoenix study) to “Very Poor” 

(29 dv or greater, which less than 10% of the study participants rated as acceptable). The 

“Good” range is 15 dv to 20 dv (more than 90% of the participants rated images in this VAQ 

range as acceptable). The environmental goal of the Phoenix urban visibility program is to 

achieve continued progress through 2018 by moving the number of days in poorer quality 

categories into better quality categories. 

2.5 WASHINGTON, DC 

One of the Washington, DC urban visibility pilot studies was conducted on behalf of 

EPA (Abt Associates Inc., 2001). It was designed to be a pilot focus group study, an initial 
2-16

developmental trial run of a larger study. The intent of the pilot study was to refine both focus 

group method design and potential survey questions. Due to funding limitations, only a single 

focus group session took place, consisting of one extended session with nine participants. No 

further urban visibility focus group sessions were held in Washington, DC on behalf of EPA. 

In March 2009, Dr. Anne Smith conducted a separate study of Washington urban 

visibility, using the same photographs and similar approach as the 2001 study (Smith and 

Howell, 2009). On behalf of the Utility Air Regulatory Group, Dr. Smith presented comments 

(Smith, 2009) to the CASAC at a public meeting held on April 2, 2009 to review EPA’s plan 

(US EPA, 2009b) for conducting further urban visibility studies in support of PM NAAQS 

reviews. Dr. Smith submitted the Smith and Howell (2009) report to the CASAC as part of the 

public comment process. The Smith and Howell study conducted three study variations of a 

Washington, DC preference study, including one experiment involving 26 participants designed 

to replicate the EPA pilot study (Abt Associates Inc., 2001). Both the Abt Associates Inc. 

(2001) study results and the results of the Smith and Howell (2009) study are discussed below. 

2.5.1 Washington, DC 2001 

The EPA’s Washington, DC study (Abt Associates Inc., 2001) adopted the general study 

methods used in the Denver, BC, and Phoenix studies, modifying them appropriately to be 

applicable in an eastern urban setting. Washington’s (and the entire East’s) current visibility 

conditions are typically substantially worse than western cities and have different characteristics. 

Washington’s visibility impairment is primarily a uniform whitish haze dominated by sulfates, 

and the relative humidity levels are higher compared with the western study areas. In addition, 

the relatively low-lying terrain3

 in Washington, DC provides substantially shorter maximum 

sight distances. 

The Washington, DC focus group session included questions on valuation, as well as on 

preferences. The focus group content dealing with preferences for an urban visibility standard 

was similar to the focus group sessions in the western studies. 

A single scene of a panoramic photograph taken from Arlington National Cemetery in 

Virginia was used, and included an iconic view of the Potomac River, the National Mall, and 

downtown Washington, DC. All of the distinct buildings in the scene are less than four miles 

from the camera, and the higher elevations in the background are less than 10 miles from the 

camera. Figure 2-8 presents the photograph with the best VAQ used in the study. 

 

3

The maximum elevation in Washington, DC is 409 feet. 
2-17

Figure 2-8. Reproduction of the Image with the Best VAQ (8.8 dv) Used in the 

Washington, DC Study 

The Washington, DC study used 20 unique images generated by WinHaze, each prepared 

from the same original photograph. Humidity and gaseous light scattering was held constant in 

preparing the WinHaze images, as was the relative chemical mix of aerosol particulates in the 

photos (i.e., only the aerosol concentrations were increased to create the images with worse 

VAQ). Five of the images were repeated as a consistency check, so participants viewed a total 

of 25 slides. The VAQ in the images ranged from 8.8 to 38.3 dv Figure 2-9 presents the percent 

acceptability results from the 2001 Washington study. Because only nine participants were 

involved in the study, the possible values of “percent acceptable” are limited to multiples of 1/9. 

Figure 2-9 also shows an anomalous result involving one of the five repeated images. Three of 

the repeat images had the same ranking each time they were presented (i.e., all nine participants 

rated them acceptable or not acceptable both times they rated that slide). One of the images (the 

image with 8.8 dv, the best VAQ image used in the study) was rated acceptable by all nine 

participants the first time it was used, but the repeat of that slide was rated not acceptable by one 

participant. Another image, however, had a substantially different result. The 30.9 dv image 

was rated acceptable by five of the nine participants the first time it was presented, but the repeat 

of the slide was only rated acceptable by one of the nine participants. The responses for all five 

pairs of repeated images are shown in red on Figure 2-9, including the images which were 

identically rated both times they were presented. 
2-18

Figure 2-9. Percent of 2001 Washington Participants Who Considered VAQ Acceptable 

in Each Image

Results of 2001 Washington DC Preference Study

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating 

"Acceptable"

Unique Images 5 Repeated Images

In the 2001 Washington, DC study, all images with a VAQ below 25.9 dv were rated 

acceptable by the majority of the participants, and all images with a VAQ below 29.2 dv were 

rated acceptable by at least four of the nine (44%) participants. All images with a VAQ above 

30.9 dv were rated not acceptable. The “50% acceptability criteria” division occurs in the range 

of 25.9 dv to 30.9 dv, with the anomalous result of the inconsistent responses to the repeated 

image with 30.9 dv effectively broadening this range and adding uncertainty to identifying a 

clear division. 

2.5.2 Washington, DC 2009 

The Smith and Howell (2009) study conducted additional focus group sessions based on 

the methods and materials used in the 2001 Washington, DC study. Smith and Howell recreated 

the WinHaze images used in the 2001 Washington, DC urban visibility preference study, using 

the description in the report on the 2001 study (Abt Associates Inc., 2001), and created images 

using currently available desktop computer version of WinHaze (Version 2.9.0). Smith and 

Howell used a shortened version of the same question protocol as the 2001 study. The WinHaze 

images were presented to a total of 64 participants who were all employees of Charles River 

Associates (CRA International, Inc). (Smith and Howell also are CRA International employees). 

The CRA employees were based at the firm’s Washington, DC and Houston, Texas offices (44 
2-19

and 20 participants, respectively). The Houston participants were included to explore whether 

familiarity with Washington, DC VAQ conditions developed from currently living in the 

Washington region noticeably influenced the responses. As noted by Smith and Howell, the 

participants were not a representative sample of either metropolitan area’s population; all 

participants were employed, and the participant group included a higher proportion of college 

educated individuals and higher household incomes than the general population. 

Eight of the Washington-based participants and all of the Houston participants viewed the 

WinHaze images on a desktop computer monitor. The remaining Washington participants 

viewed the images projected on a screen. 

The stated purpose of the Smith and Howell study was to explore the robustness of the 

2001 results. To investigate this issue, Smith and Howell conducted three different tests 

concerning urban visibility preferences. Each participant was involved with only one test. The 

three tests were: 

 Test 1 - replicated the Abt Associates Inc. (2001) study 

 Test 2 - reduced the upper end of the range of VAQ by eliminating the 11 images 

used in Test 1 with a VAQ above 27.1 dv 

 Test 3 - increased the upper end of the range of VAQ by including two new images 

of worse VAQ; the two new images had a VAQ of 42 dv and 45 dv 

Sixteen employees from the Washington, DC office and 10 participants from the Houston 

office took Test 1 (a total of 26 participants). All the participants viewed the same unique 20 

Washington, DC WinHaze images as the 2001 study (plus repeated images for a total of 25 

images shown to participants). Images were presented in the same random order as in the 2001 

study. Figure 2-10 presents the results of Test 1. The results for the 16 Washington participants 

are indicated in blue and results for the 10 Houston participants in red. Although all images used 

in the study were of Washington, DC, the results suggest that there is not a significant difference 

in the preferences of participants based in the two offices. The scene in the images is an 

immediately recognizable iconic view of the National Mall and downtown Washington, DC, 

which may influence the similarity of responses by residents of the two cities. 
2-20

Figure 2-10. Percent of 2009 Test 1 Study Participants Who Considered VAQ 

Acceptable in Each Image, Showing the Range of the Lower and Upper 

Bound of 50% Acceptability Criteria 

Using the combined Test 1 results from the two CRA offices (26 total participants), the 

majority of participants in the 2009 study rated all VAQ images with 25.9 dv or less as 

acceptable and all VAQ images with 29.2 dv or greater as not acceptable. The image of 27.1 dv 

was rated as acceptable by 50% of the total participants (56% of the Washington-based and 40% 

of the Houston-based participants). All images with a VAQ less than 22.9 dv were rated 

acceptable by at least 90% of the participants, and all images with a VAQ greater than 32.3 dv 

were rated not acceptable by 88% of the participants. 

Figure 2-11 presents the Abt Associates Inc., 2001 study and Smith and Howell 2009 

(Test 1) study results on a single graph, representing the results of 35 total participants of 

preferences for urban visibility in Washington, DC. The results from the 2009 study on Figure 

2-11 combine the Test 1 responses from the two CRA offices. Figure 2-11 also shows the 50% 

acceptability criteria range (22.9 dv to 32.3 dv) from the 2009 Test 1. In comparison, the 2001 

study 50% acceptability range was 25.9 dv to 30.9 dv. Inspection of the points in Figure 2-11 

indicates that the results from the 2009 study (Test 1) are not appreciably different than the 

results of the 2001 Washington study. This observation of similar results is confirmed by a logit 

regression analysis of the 2001 and 2009 Test 1 data that includes estimates of the 50% criteria 
2-21

deciview values with confidence intervals and hypothesis testing of the similarity of the values, 

as described in Appendix J (see Tables 6, 7 & 8 and Figure 3). 

Figure 2-11. Combined Results of Washington, DC 2001 and 2009 Test 1 (showing 50% 

Acceptability Criteria from 2009, Test 1)

0%

50%

100%

5 10 15 20 25 30 35 40

% Participants Rating "Acceptable"

Deciview

2001 Study 2009, Test 1 Lower Bound Upper Bound

In Test 2, Smith and Howell reduced the range of VAQ images to images with a VAQ of 

27.1 dv or less. The 26 participants in the Test 2 study were different people than the Test 1 

participants. Test 2 presented only the nine unique clearest WinHaze images from the full Test 1 

set of 20 images, along with 3 duplicates for a total of 12 images. This constricted the VAQ 

levels presented to the range that the majority of participants in the 2001 study rated as 

acceptable and reduced the upper end of the VAQ range by 11.2 dv. 

Figure 2-12 presents the Test 1 and Test 2 results. Test 2 found a substantial shift in the 

responses regarding which VAQ levels are considered acceptable. The smaller number of 

images used in Test 2 made identifying the range of the 50% acceptability criteria more difficult 

than in Test 1. The lower bound of the range occurs between 15.6 and 18.7 dv, and the upper 

bound occurs between 24.5 and 27.1 dv. Smith and Howell conclude that the shift in the 

acceptability responses between Test 1 and Test 2 suggests that the VAQ levels identified as 

acceptable in an urban visibility preference study conducted using the general approach 

previously used in the all the studies may be influenced by the range of VAQ images presented. 
2-22

Figure 2-12. Comparison of Results from Test 1 and Test 2 (Smith and Howell, 2009)

Results of Smith and Howell Test 1 and Test 2

0%

50%

100%

5 10 15 20 25 30 35 40

Deciview

% Participants Rating "Acceptable"

Test 2 Test 1

In order for the range of images shown to be able to influence the acceptability ratings, 

participants would need to be aware of the upper and lower bounds of the range prior to the 

judging acceptability. However, since they were shown images randomly with respect to the 

VAQ levels, asked to rate each one before going to the next image, and were not given a chance 

to revise their acceptability ratings, this was not possible during the acceptability exercise itself. 

The only other opportunity participants could have to learn the VAQ range is during the VAQ 

rating exercise done just prior to the acceptability rating. However, in the VAQ rating exercise 

where the participants were asked to rate the quality of visibility for the shown images on a scale 

from 1 to 7, the images were also shown in a random order, participants were not aware how 

many photographs would be shown or the range of conditions, they were asked to rate each one 

using a value from 1 to 7 before going on to the next image and they did not have the opportunity 

to revise the ratings of earlier viewed images. 

Figure 2-13 shows the average visibility rating on the 1 to 7 scale for each image used in 

each of the three tests conducted by Smith and Howell (2009). The consistency observed in the 

relationship between VAQ deciview levels and the average scores assigned across the three tests 

demonstrates that the participants come to the survey with the capability to consistently rate the 

haze levels shown in the images, regardless of the breadth of the range used or the order or 
2-23

number of slides shown, and that they are aware of a full range of conditions, even when, as was 

the case in Test 2, they were not shown the worst haze images. 

Figure 2-13. Average Visibility Ratings for the Washington, DC WinHaze Images 

by Participants in Tests 1-3 Conducted by Smith and Howell (2009). 

Why then did Test 2 participants in the subsequent part of the survey rate images of haze 

levels as unacceptable that were rated acceptable by participants in the other tests and the earlier 

Washington, DC pilot study? In a three sentence script4 that constituted the only instructions 

read prior to the acceptability rating, the participants were told that they would see the same set 

of slides that they had just rated (i.e., on the 1 to 7 scale), and they were asked to rate them 

according to whether the VAQ depicted were acceptable or unacceptable to them. Apparently by 

directing then to rate the same images for acceptability, the participants understood that their 

choices of visibility conditions were restricted to a range of conditions shown in the 1 to 7 

ratings that they had just completed. For participants in Test 2 this would mean that by their own 

1 to 7 ratings the range was restricted to include no poor visibility conditions (i.e. only scenes 

rated from 3 to 7). 

Smith and Howell (2009) concluded that the effects of a changed range on the 

acceptability ratings results demonstrates that VAQ preference studies results are not robust and 

 

4

 The complete script for the acceptability/unacceptability part of the study is as follows. “Now you will be 

shown the same set of slides that you just rated. Again each image will illustrate the effects of a different level of 

visibility. This time, rate the slides according to whether the visibility is acceptable or unacceptable to you.”
2-24

do not reflect an enduring view on the “unacceptability” of different levels of VAQ degradation. 

However, there is an alternative explanation. It seems more likely that the use of such a severely 

truncated range of VAQ conditions in Test 2, which did not include any of the images of VAQ 

that previous studies identified as unacceptable, in effect fundamentally changed the implied 

instructions for the participants. Instead of conveying that they were to identify VAQ levels that 

they found acceptable among a full range of VAQ conditions from very poor to very good, the 

implied message was that they should identify the VAQ levels that they found acceptable among 

a curtailed range of VAQ conditions that only included average to very good VAQ. By this 

reasoning, it would be inappropriate to include Test 2 results with those of the other tests as a 

measure of VAQ preference for Washington, DC. 

In Test 3, Smith and Howell expanded the VAQ range of WinHaze images shown to the 

participants, including two new images with a worse VAQ. The new images had a VAQ of 42 

dv and 45 dv, raising the upper end of the VAQ range by 6.7 dv. Test 3 also reduced the total 

number of images shown to participants to 19 images by eliminating the use of the five repeat 

images in Test 1, and also eliminated three additional images in order to reduce the participants’ 

time burden. The three deleted images had a VAQ of 11.1, 15.6, and 24.5 dv. The best VAQ 

image shown to Test 3 participants was 8.8 dv (same as the best VAQ image in Tests 1 and 2). 

However, in Test 3 there were no images with VAQ between 8.8 dv and 18.7 dv, creating a 

significant “hole” in the distribution of VAQ conditions presented to the Test 3 participants. 

Test 3 was conducted with 12 participants from the CRA Washington office (none of whom 

participated in Test 1 or Test 2). No Houston participants were involved with Test 3. Figure 2-

13 shows that the Test 3 average ratings from 1 to 7 during the VAQ rating exercise increased 

the average participant rating by about 1 at the low end of the scale (very poor VAQ). The 

results of Test 3 are shown in Figure 2-14, along with the results of Test 1. 

Test 3 resulted in an overall increase in the percent of respondents rating as acceptable 

the VAQ images used in both tests. In Test 3 all images with a VAQ below 22.9 dv were rated 

acceptable by 100% of the participants (similar to the Test 1 results), implying there was no 

general change in the acceptability of the images with good VAQ. However, for all VAQ 

images (that were used in both studies) between 25.9 dv and 33.6 dv, a noticeably larger 

percentage of the participants in Test 3 rated the image as acceptable than in Test 1. At VAQ 

levels worse than 33.6 dv, the majority of the participants found the VAQ level not acceptable in 

both tests. 
2-25

Figure 2-14. Comparison of Results from the Smith and Howell (2009) Test 1 and Test 3

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating "Acceptable"

Test 3 Test 1

Given that most of the same images of VAQ conditions were used in all of the tests, 

composite acceptability ratings (i.e., from the original pilot study (2001) and from Tests 1 and 3) 

of each image were initially developed to evaluate whether increasing the number of participant 

ratings for each image influenced the 50% acceptability value. Figure 2-15 shows composite 

results from the combination of these three groups (total of 47 participants). The 50% 

acceptability criteria value for this composite dataset lies unambiguously between the 30.1 dv (at 

51.1%) and the 30.9 dv points (at 46.3%), 
2-26

Figure 2-15. Composite Results from Smith and Howell (2009) Tests 1 and 3, and Abt 

(2001) Washington, DC Pilot Study 

0%

50%

100%

0 10 20 30 40 50

%

A

c

c

e

p

t

a

b

l

e

Deciview

Washington DC Studies Combined

2001 and Smith Tests 1 & 3

To determine whether it would be appropriate to combine all of the results from the 

Washington, DC studies, in order to increase the number of data points for Washington, DC, we 

considered several factors. First, while the range limitations identified with Test 2 (i.e. an overly 

restrictive range) that resulted in its results being eliminated from consideration in the selection 

of appropriate CPLs do not apply to Test 3 results due to its somewhat more complete coverage 

of the 1 to 7 rating range in the VAQ rating exercise, the number of participants in Test 3 (i.e., 

12) is small enough that the statistical uncertainty of the results may be an issue if used alone. 

Second, it was not clear whether the significant “hole” in the Test 3 VAQ distribution between 

8.8 dv and 18.7 dv potentially had an effect on participant acceptability responses. Finally, the 

logit regression analysis which was applied to each of the individual Smith and Howell (2009) 

tests as well as subsets of some of these tests to investigate differences in the preference curves 

and 50% criteria deciview levels (Appendix J) concluded in part that Test 2 and Test 3 response 

curves and 50% criteria values are statistically dissimilar from those of the 2001 and Test 1 

Washington, DC studies. In contrast, the logit analysis concluded that the 2001 and Test 1 

results have 50% criteria values that are statistically indistinguishable. These findings provided 
2-27

support for combining the 2001 and Test 1 data sets and for excluding the dissimilar results of 

Test 2 and Test 3. 

2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF 

CANDIDATE PROTECTION LEVELS 

As described above, because each of the studies reviewed in this assessment investigates 

a common question and use similar approaches that are all derived from the method first 

developed for the Denver urban visibility study, we concluded that it is reasonable to compare 

the results from all four urban areas to identify overall trends in the study findings and that this 

comparison can usefully inform the selection of CPLs for use in further analyses. However, 

because variations in the specific materials and methods used in each study introduce 

uncertainties, direct comparison of the study results should take these factors into account. Key 

differences between the studies include: 

 Image presentation methods (e.g., projected slides of actual photos, projected images 

generated using WinHaze (a significant technical advance in the method of presenting 

VAQ conditions), use of computer monitor screen 

 Number of participants in each study, 

 Participant representativeness of the general population of the relevant metropolitan 

area, and 

 Specific wording used to frame the questions used in the group interview process. 

Figure 2-16 presents a graphical summary of the results of the studies in the four 

cities and draws on results previously presented in Figures 2-3, 2-5, 2-7 and 2-11. As 

described in the separate discussions for each urban area above, the data and curves 

depicted in Figure 2-16 include the following modifications: 1) the Denver results omit 

the 9:00 a.m. photograph results; 2) the Chilliwack and Abbotsford results appear as a 

single set of data for the BC study; 3) the results from 2001 and 2009 (Test 1) studies of 

VAQ preferences in Washington, DC are presented as a single combined set of data; 4) 

the results from the 2009 Washington, DC study Tests 2 and 3 are not included. 
2-28

Figure 2-16. Summary of Results of Urban Visibility Studies in Four Cities, Showing the 

Identified Range of the 50% Acceptance Criteria5

Figure 2-16 shows the results of a logistical regression analysis using a logit model of the 

greater than 19,000 ratings of haze images as acceptable or unacceptable. The logit model is a 

generalized linear model used for binomial regression analysis which fits explanatory data about 

binary outcomes (in this case, a person rating a VAQ image as acceptable or not) to a logistic 

function curve. A more complete description of the logit model application to these data is 

contained in Appendix J. The results shown in Figure 2-16 are from the more generalized of the 

two logit assessment models (i.e. model 2) in which both the shape and displacement of the 

curves for the four cities are permitted to vary independently. 

The logit analysis city intercept coefficients (Appendix J, Table 3) are all positive and 

statistically significant, indicating that the response functions for different cities shifted right 

relative to the function for Denver. However, only the Phoenix interaction term is insignificant, 

 

5

 Top scale shows light extinction in inverse megameter units; bottom scale in deciviews. Logit analysis 

estimated response functions are shown as the color-coded curved lines for each of the four urban areas. 
2-29

indicating that the Phoenix response function has a different shape that is steeper than the other 

three cities, as can be seen in Figure 2-16. Figure 2-16 also shows the Washington, DC function 

is modestly less steep than the others, but the decrease in the slope is not statistically significant. 

The model results can be used to estimate the VAQ deciview values where the estimated 

response functions cross the 50% acceptability level, as well as any alternative criteria levels. 

Selected examples of these are shown in Table 2-3. A t-test of these 50% acceptance deciview 

values for the four cities shows each to be significantly different from the others (Appendix J, 

Table 5). 

Table 2-3. Logit Model Estimated VAQ Values Corresponding to Various Percent 

Acceptability Values for the Four Cities 

Denver 

British 

Columbia Phoenix 

Washington, 

DC 

90% Acceptability criteria 14.21 16.80 24.15 23.03 

75% Acceptability criteria 17.05 19.63 21.80 26.03 

50% Acceptability criteria 19.90 22.45 24.15 29.03 

25% Acceptability criteria 22.74 25.28 26.51 32.03 

10% Acceptability criteria 25.59 28.10 28.87 35.03 

Figure 2-16 also contains lines at 20 dv and 30 dv that effectively and pragmatically 

identify a range where the 50% acceptance criteria occur across all four of the urban preference 

studies. Out of the 114 data points shown in Figure 2-16, only one photograph (or image) with a 

VAQ below 20 dv was rated as acceptable by less than 50% of the participants who rated that 

photograph.6

 Similarly, only one image with a VAQ above 30 dv was rated acceptable by more 

than 50% of the participants who viewed it.7

 These upper and lower range values are also 

supported by the logit model data which estimates 50th percentile acceptability values near 20 dv 

for Denver and near 30 dv for Washington, DC (see Table 2-4). 

There are several hypotheses that may explain why the VAQ acceptability response 

curves for the four cities are different and why some study results have greater variability than 

 

6

 Only 47% of the BC participants rated a 19.2 dv photograph as acceptable. 7

 In the 2001 Washington, D.C. study, a 30.9 dv image was used as a repeated slide. The first time it was 

shown 56% of the participants rated it as acceptable, and 11% rated it as acceptable the second time it was shown. 

The same VAQ level was rated as acceptable by 42% of the participants in the 2009 study (Test 1). 
2-30

others.8

 First, as mentioned, the use of photographs (Denver and BC surveys) versus WinHazegenerated images (Phoenix and Washington, DC surveys) may play a significant role in 

preference studies, perhaps introducing bias (such as suggested by the responses to the 9:00 a.m. 

Denver photographs) as well as variability. Further, the use of photographs from different days 

and times of day that rely on associated ambient measurements of light extinction to characterize 

their VAQ level can introduce two other types of uncertainty. The intrinsic appearance of the 

scene can change due to the changing shadow pattern and cloud conditions, and spatial variations 

in air quality can result in ambient light extinction measurements not being representative of the 

sight-path-averaged light extinction. WinHaze has neither of these sources of uncertainty 

because the same base photograph is used (i.e. no intrinsic change in scene appearance) and the 

modeled haze that is displayed in the photograph is determined based on uniform light extinction 

throughout the scene. 

Second, variation in the degree of representativeness of the participants and the sizes of 

the participant samples involved may also be important factors. The small sample size and fairly 

uniform population of respondents is a plausible explanation for the noisiness of the combined 

Washington, DC results (35 participants, including 26 from a single consulting firm and 10 of 

those from a different city) compared with the larger and more representative population of 

responders from Phoenix (385 participants, carefully selected to be representative of the Phoenix 

population). 

A third hypothesis put forward by Smith and Howell (2009) is that the range of VAQ 

images presented in the survey may influence the results. As discussed above, a more plausible 

explanation it that the range of haze images shown to participants in the VAQ 1 to 7 rating 

exercise was interpreted by participants as a restriction on acceptability rating exercise to confine 

their rating to the range VAQ conditions shown, which for Test 2 was curtailed to only average 

to good VAQ conditions. When other evidence is taken into account, the Smith and Howell 

hypothesis seems an even more unlikely explanation for the differences in results between the 

four urban preference studies. For example the Denver study included photographs with the 

haziest conditions among the four studies, but resulted in the lowest haze condition for the 50th

percentile preference ratings among the four, not the highest as might be expected if the range of 

haze levels were a significant factor influencing the results of preference studies. Also, 

inspection of the average VAQ 1 to 7 ratings for the Phoenix and Denver studies showed that 

they spanned the full ratings range of values similar to those for the Smith and Howell Test 1 and 

3, so the participants in those studies were not presented with a restricted range within which to 

select acceptable VAQ conditions, suggesting that the range itself was not an important factor 

 

8

 Variability here refers to the degree of scatter of the average acceptability ratings for each image around 

the logit curve for that city. 
2-31

influencing their results. Values for the British Columbia 1 to 7 VAQ rating exercise were not 

readily available. 

A fourth major hypothesis is that urban visibility preferences may differ by location, and 

the differences may arise from inherent differences in the cityscape scene used in each city. The 

key evidence to suggest this hypothesis is that the apparent differences between the Denver 

results (which found the 50% acceptance criteria occurred in the best VAQ levels among the four 

cities) and the Washington, DC results (which found the 50% acceptance criteria occurred at the 

worst VAQ levels among the four cities). This hypothesis suggests that these results may occur 

because the most prominent and picturesque feature of the cityscape of Denver is the clearly 

visible snow-covered mountains in the distance, while the prominent and picturesque features of 

the Washington, DC cityscape are buildings relatively nearby without prominent and/or valued 

scenic features that are more distant. 

Finally, and perhaps of significant importance is that the sensitivity of individual scenes 

to perceived changes in VAQ under changing light extinction levels can be quite different. As in 

the fourth hypothesis, this may in part explain why the Denver study scene, with its long distance 

to the mountain backdrop, resulted a preference for the best VAQ level, with a 50% criteria value 

of about 20 dv, while the Washington, DC study scene, with much shorter sight paths yielded a 

50% criteria VAQ value at a substantially worse level of about 30 dv. The distinction between 

the last two hypotheses are that the earlier one speaks to the desirability of seeing distant 

mountains versus this hypothesis which concerns the ability to perceive changes in haze at lower 

light extinction levels. Additional studies, including directly comparable studies using similar 

methods in diverse cities, would be useful to gain further understanding of preferences for urban 

visibility. 

Based on the composite results and the effective range of 50th percentile acceptability 

across the four urban preference studies shown in Figure 2-16, CPLs have been selected in a 

range from 20 dv to 30 dv (74 Mm-1 to 201 Mm-1) for the purpose of comparing to current and 

projected conditions in the assessment in chapters 3 and 4 of this document. A midpoint of 25 

dv (122 Mm-1) was also selected for use in the assessment. These three values provide a low, 

middle, and high set of light extinction conditions that are used in subsequent chapters of the 

UFVA to provisionally define daylight hours with urban haze conditions that have been judged 

unacceptable by the participants of these preference studies. As discussed earlier (section 1.2), 

PM light extinction is taken to be light extinction minus the Rayleigh scatter (i.e. light scattering 

by atmospheric gases is about 10 Mm-1), so the low, middle and high CPL levels correspond to 

PM light extinction levels of about 64 Mm-1, 112 Mm-1 and 191 Mm-1. 
This page left intentionally blank. 
3-1

3 ESTIMATION OF RECENT PM MASS AND SPECIES 

CONCENTRATIONS AND PM10 LIGHT EXTINCTION 

This chapter characterizes hourly PM conditions in terms of both PM2.5 mass 

concentration and PM10 light extinction in a set of urban study areas during 2005-2007. This 

characterization supports the following goals: (1) to improve understanding of the levels, 

patterns, and causes of PM-related impairment of urban visibility during daylight hours; (2) to 

create the basis for projections of PM2.5 mass and PM10 light extinction levels under “what if” 

scenarios; and (3) to examine the correlation between PM10 light extinction and potential 

alternative indicator(s) based on PM2.5 mass concentration. These goals are addressed in 

chapters 3, 4 and Appendix D, respectively. A number of other appendices address related topics 

of particular interest in more detail. 

3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM 

CONCENTRATIONS AND LIGHT EXTINCTION 

3.1.1 PM2.5 and PM10-2.5

Chapter 2 of the 2005 Staff Paper (US EPA, 2005) from the previous review and chapters 

3 (especially section 3.5) and 9 (especially section 9.2.3) and Annex A of the final ISA (US 

EPA, 2009a) from the current review present extensive characterizations of the levels, 

composition, and temporal and spatial patterns of PM2.5 in U.S. urban areas. Both documents 

present data summaries based on the approximately 1000 PM2.5 monitoring sites in the U.S. The 

characterizations in the 2005 Staff Paper were based on 2001-2003 data. The characterizations 

in the ISA are based on 2005-2007 data, which is the same time period used in this visibility 

assessment. While there generally have been reductions in the concentrations of PM2.5 in many 

areas as a result of emission reductions of PM2.5 and its precursors, the general patterns, and the 

diversity of patterns across areas, noted in the 2005 Staff Paper still prevailed in the 2005-2007 

period. 

Using 2005-2007 air quality data, 38 urban areas violated the annual PM2.5 NAAQS set at 

a level of 15μg/m3

 in 1997 and retained in the last review completed in 2006. Seventy-six areas 

violated the 2006 24-hour NAAQS level of 35μg/m3. There is considerable but not complete 

overlap in the areas not meeting the two NAAQS. It should be noted that in many parts of the 

U.S., PM2.5 concentrations in 2005 were high relative to the next three years. Figure 3-1 

illustrates PM2.5 air quality in 2007 by representing each monitor by a symbol whose color 

reflects the annual mean of the concentration at that site or the 98th percentile 24-hour 

concentration, in both cases in that one year. 
3-2

Figure 3-1. Annual Average and 24-hour (98th Percentile 24-hour Concentrations) PM2.5 

Concentrations in μg/m3, 2007. 
3-3

Each urban area exhibits its own detailed patterns of observed concentration levels, 

temporal and spatial variation, and composition. These differences are due to differences in local 

and transported emissions and in meteorology. Because of differences in the placement of PM2.5

monitoring sites in each urban area, the actual levels and spatial pattern of PM2.5 and PM2.5

species concentrations may not be consistently discernable in all areas. This variability and 

limited monitoring network make it difficult to offer concise generalizations, although some 

broad similarities can be drawn among areas. 

Midwestern, southeastern, and eastern urban areas have much higher sulfate levels than 

do more western areas, attributable to the much higher emissions of SO2 in and upwind of them. 

Areas in the upper Midwest and to a lesser extent upper East have notable nitrate concentrations 

in winter but not in summer, while southeastern areas generally have lower nitrate concentrations 

even in winter. Many western urban areas have large nitrate concentrations year round. In all 

areas, carbonaceous material is an important component of PM2.5 and is attributable to many 

emission sources of organic material in PM form and of organic PM precursor gases. In some 

areas with high local use of wood for residential heating carbonaceous material is dominant 

during the heating season. PM2.5 derived from crustal sources is generally a small fraction of 

total mass, except during local high wind events or due to brief periods of intercontinental 

transport of dust from Africa or Asia. 

Comparison of PM2.5 species concentrations within and outside urban areas leads to the 

conclusion that, in the eastern areas with high sulfate concentrations, the large majority of the 

sulfate affecting any given urban area originates outside that area. Inward transport and local 

generation of nitrate and carbonaceous material are more evenly balanced in eastern areas, with 

some differences among areas. In western areas, local sources dominate for carbonaceous 

material and nitrate, with the origins of the small sulfate component being more balanced. See 

Figure 9-24 of the final ISA (US EPA, 2009a). 

Southeastern areas have their highest PM2.5 concentrations in the summer, when 

conditions are most conducive to sulfate formation. More northern areas, being affected by a 

more balanced mix of contributors, tend not to have such a strongly seasonal pattern. The 

seasonal patterns in western areas are individual and varied, related to differences in local 

sources and formation and dispersion conditions. In all areas, inversion conditions with low 

wind speeds are conducive to high concentrations due to the trapping of emissions from local 

sources. Some western areas, especially those with valley or bowl-like topography, are 

especially affected. 

There is at present no systematic monitoring network in place for PM10-2.5, as states have 

until January 1, 2011, to implement required monitoring sites for PM10-2.5. Consequently, 

estimates of PM10-2.5 must be developed using data from PM2.5 and PM10 monitoring sites and 
3-4

equipment, which are not always collocated and consistent. The 2005 Staff Paper presented such 

estimates in section 2.4.3. The final ISA presents such estimates in Figure 3-10 and Table 3-9 of 

section 3.5.1.1. The 2005 Staff Paper used a data-inclusive approach in which the best available 

data on PM2.5 and PM10 concentrations – in some cases not very robust data – were used to 

estimate 2001-2003 PM10-2.5 concentrations for 351 metropolitan area counties. For these 

counties, the annual mean PM10-2.5 concentrations were generally estimated to be below 40 

μg/m3, with one maximum value as high as 64 μg/m3 and a median of about 10-11 μg/m3. The 

ISA used a much more data-restrictive approach based only on paired (collocated) low-volume 

filter-based samplers for both PM10 and PM2.5. The ISA reports that only 40 counties have such 

paired samplers. Using these available co-located PM measurements from 2005-2007, the mean 

24-hr PM10-2.5 concentration in these 40 counties was 13 μg/m3

. This urban visibility assessment 

has used a data-inclusive approach to estimating PM10-2.5 concentrations, similar to that used for 

the 2005 Staff Paper, where needed to obtain hourly PM10-2.5 estimates for the 15 selected study 

areas, which are reported below in section 3.3.2. 

Additional detail on PM2.5, PM10, and PM10-2.5 concentrations, composition, and patterns 

appears in section 3.5.1.1 of the ISA. Also, chapter 6 of the 2004 PM Assessment by NARSTO 

contains more detailed characterizations of PM in different parts of the U.S. 

3.1.2 PM10 Light Extinction 

While total light extinction is directly measurable using a transmissometer and PM10 light 

extinction can be measured with other instruments, there are very few regularly operating 

monitoring sites measuring either form of light extinction in urban areas, and generally those that 

do operate do not submit data to AQS.1 Consequently, any characterization of PM10 light 

extinction conditions based on actual measurements is necessarily less comprehensive than for 

PM2.5 and PM10-2.5. Many monitoring sites that employ nephelometers, which measure light 

scattering, operate that equipment in a heated mode for purposes of tracking “dry” PM2.5 mass 

concentrations, and actual light scattering due to ambient PM is not reportable. There are many 

more filter-based Aethalometers® and similar instruments for measuring light absorption in 

operation and reporting to AQS, but light absorption is typically a small fraction of total PM 

light extinction, so these data alone are not a good indicator of overall PM10 light extinction in 

 

1

 EPA is aware of routine, long-term direct measurement of light extinction using transmissometers only in 

the Phoenix, AZ, Denver, CO, and Washington, DC urban areas, none of which submit data to AQS, although the 

site in Washington submits data to the IMPROVE program data system. Also, there is a large network of “visual 

range” monitors in operation at U.S. airports, aimed at providing information to determine landing and takeoff 

safety. Due to their locations and to the lack of data resolution (values of visual range above the level needed for 

unlimited airport operations are not individually reported) the data from these monitors are not suitable for use in 

this assessment. The final PM ISA discusses these monitors in section 9.2.2.3. 
3-5

urban areas. Also, there are unresolved issues of data corrections and comparability for the light 

absorption data from these instruments now residing in AQS. 

PM10 light extinction can be “reconstructed” from measurements of PM2.5 mass 

components and PM10-2.5 concentrations, in combination with relative humidity values, using 

either of two versions of the formula known as the IMPROVE algorithm but excluding its term 

for Rayleigh scattering by gases in clean air. (Section 9.2.2.2 of the ISA gives an overview of 

the algorithm and its basis. Section 3.2.3 of this document discusses the application of the 

original version of the IMPROVE algorithm in this assessment. PM2.5 component measurements 

are generally available only on a 24-hour average basis, so it generally is possible to estimate 

only 24-hour average PM10 light extinction, unless additional information on hourly patterns is 

brought to bear.2

 Because EPA’s Regional Haze Rule (RHR) currently requires states to address 

visibility problems in Class I visibility protection areas, which are nearly all rural and remote, 

there is a large body of literature characterizing PM10 light extinction in remote rural areas, based 

on data from the IMPROVE network’s 24-hour samplers and on special studies. Sections 9.2.3.2 

and 9.2.3.4 of the ISA summarize this literature. Section 9.2.3.3 of the ISA contrasts 

concentrations of PM2.5 and PM2.5 components between rural and urban areas using data from the 

rural IMPROVE network and the urban Chemical Speciation Network (CSN) but does not 

present estimates of PM10 light extinction in urban areas. 

The CSN network provides 24-hour PM2.5 species measurements at about 200 urban 

sites, from which mass components can be derived. These sites have a mix of daily, one day in 

three, and one day in six sampling schedules. The 2005 Staff Paper (and its references) may be 

the only readily available prior assessment to use these urban PM2.5 speciation monitoring data, 

along with estimates of PM10-2.5 concentrations and data on relative humidity, to reconstruct daily 

24-hour average light extinction in urban areas, for the year 2003.3

 One presentation of the 

results was in the form of a scatter plot of daily 24-hour reconstructed light extinction versus 24-

hour PM2.5 concentration. This graphic appears here as Figure 3-2. (For the immediate purpose 

of this section, it is the distribution of the data points along the y-axis that is of interest, not the 

relationship between light extinction and PM2.5 concentrations; the latter subject is addressed in 

 

2

 When the IMPROVE algorithm is used to estimate 24-hour PM10 light extinction from 24-hour PM2.5

species and PM10-2.5 concentrations, an assumption is made that every hour has the same PM concentrations but its 

own relative humidity value. Hourly estimates of PM10 light extinction, including the strongly non-linear effect of 

relative humidity, are then averaged to get the 24-hour PM10 light extinction estimate. 3

 Estimates of light extinction in the 2005 Staff Paper include Rayleigh scattering of 10 Mm-1 and thus 

represent “total” light extinction (excluding NO2 absorption). Adjustment for consistency must be made before any 

close comparisons to PM10 light extinction values in this document. 
3-6

Appendix D.) Generally, most days have light extinction below 200 inverse megameters (Mm-1), 

but a small percentage of values were as high as about 750 Mm-1.

4

 

Figure 3-2. Reconstructed 24-hour Light Extinction in U.S. Urban Areas in 2003 

Source: Schmidt et al., 2005 

In addition to this scatter plot, a table developed for the previous PM NAAQS review 

presented the annual average of estimates of 24-hour reconstructed light extinction values, 

averaged across 161 urban areas grouped into seven regions (Schmidt, et al., 2005). Table 3-1 

reproduces these estimates. For regions excluding Southern California, annual average 24-hour 

 

4

 Unfortunately, the file of paired data used to create this scatter plot is no longer available, so the actual 

distribution of light extinction values cannot be described more specifically.
3-7

light extinction ranged from 73 to 118 Mm-1. The estimate of the annual average 24-hour light 

extinction for Southern California was 168 Mm-1. These estimates were based on 10-year 

average 1-hour relative humidity values and 2003 PM monitoring data. 

Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates 

by Region (Mm-1) 

Region Reconstructed 24-hour Light 

Extinction in 2003 

Northeast 108 

Southeast 98 

Industrial Midwest 118 

Upper Midwest 80 

Southwest 73 

Northwest 76 

Southern California 168 

Source: Output D.3, Schmidt et al., 2005. We note these regions were used to summarize PM2.5 patterns for the PM 

NAAQS review 1997 (US EPA, 1996b). 

Figure 3-3 is a contour map of annual average reconstructed 24-hour PM10 light 

extinction based on IMPROVE monitoring sites in 2000-2004, nearly all of which are remote 

and rural (the three urban sites in Phoenix, AZ, Washington, DC, and Puget Sound, WA are 

indicated by square symbols). A comparison of the mean urban light extinction levels by region 
3-8

listed in Table 3-1, with this map of rural light extinction indicates that in most parts of the U.S., 

light extinction levels in urban areas are notably higher than in the surrounding remote rural area, 

with the northeast and the southeast regions having the most similarity between rural and urban 

light extinction levels. This is consistent with observations of an “urban excess” of PM2.5 and 

Figure 3-3. Isopleth Map of Annual Total Reconstructed PM10 Light Extinction Based 

on 2000-2004 IMPROVE Data. 

(Source: Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the 

United States Report IV, DeBell 2006)

PM10-2.5 and with the known high regional concentrations of sulfate in these eastern areas. 

One-hour PM10 light extinction values of course vary above and below the 24-hour 

average, due to diurnal variations in PM2.5 component concentrations, PM10-2.5 concentrations, 

and relative humidity. Although PM10 light extinction was formally reconstructed on an hourly 

basis in the 2005 Staff Paper analysis for the last review cited above, the actual full strength of 

the diurnal pattern could not be discerned in that analysis because component mix was assumed 

not to vary from hour to hour. Under the unverified assumption of constant component mix and 

using actual hourly relative humidity data, the daily maximum daylight 1-hour PM10 light 

extinction values were roughly 50 percent higher than the 24-hour average PM10 light extinction 
3-9

values.5

 The new analysis presented in this document includes a closer look at diurnal patterns, 

for 15 study areas. 

3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY 

ANALYSIS 

As explained above, there are limited data from direct measurements of PM10 light 

extinction in urban areas. Consequently, this assessment has reconstructed hourly PM10 light 

extinction levels for daylight hours from values of hourly PM2.5 components, PM10-2.5, and 

relative humidity. Hourly monitoring data for PM2.5 components and PM10-2.5 are also generally 

lacking, so the estimates of these parameters have necessarily been developed from a 

combination of other available ambient monitoring data and air quality modeling results from a 

chemical transport model (CTM) run. Specifically, the ambient monitoring data starting points 

are 24-hour PM2.5 mass measured by filter-based Federal Reference Method (FRM) or Federal 

Equivalent Method (FEM) monitors6, 24-hour PM2.5 components measured by the filter-based 

monitors of the CSN, and hourly PM2.5 mass measured by continuous instruments such as the 

Tapered Element Oscillating Microbalance (TEOM), beta attenuation monitors (BAMs), and 

nephelometers, which were used at different sites. The CTM-based diurnal profiles for 

individual components, in conjunction with hourly PM2.5 measurements, are used to adjust and 

allocate the 24-hour PM2.5 components measurements to individual hours of each day, as 

described in detail below. In addition, levels of hourly PM10-2.5 mass are calculated from 

separate measurements of hourly PM10 and hourly PM2.5 if both are available, or by applying 

PM10-2.5 to PM2.5 ratios to hourly PM2.5 data if both types of hourly measurements are not 

available. The ambient data are from 2005-2007 and were all obtained from AQS in the first half 

of 2009. 

The CTM run was the “actual emissions” or “validation” run of the 2004 CMAQ 

modeling platform with boundary conditions provided by GEOS-Chem global scale CTM.7

 The 

CTM modeling is used as one element in the development of realistic diurnal variations for each 

of the major PM2.5 components used to estimate PM10 light extinction, anchored to site-specific, 

day-specific measurements of 24-hour concentrations. That is, monthly averaged diurnal 

profiles for the five major components were generated using the CTM results, which were then 

 

5

 These observations on diurnal patterns come from examination of “Output D.3 (Relationship RE & PM2.5; 

Diurnal RE; Timeframe) 8 of 30” and “Output D.3 (Relationship RE & PM2.5; Diurnal RE; Timeframe) 17 of 30”, 

Analyses of Particulate Matter (PM) Data for the PM NAAQS Review, Schmidt et al., 2005. 6

 Filter-based Federal Reference Method samplers and filter-based Federal Equivalent Method samplers 

will both be referred to as FRM samplers in the remainder of this document. 7

 GEOS-Chem is the NASA Goddard Earth Observing System-CHEMistry (global 3-D CTM for 

atmospheric composition). This modeling platform, with an appropriately different emissions scenario, is also the 

basis for the estimates of policy relevant background concentrations of PM2.5 presented in section 3.6 of the ISA (US 

EPA, 2009a). 
3-10

combined with hour-specific measurements of PM2.5 to generate hourly concentration variations 

for each of the 24-hour CSN sample days during the 2005-2007 period. 

3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM 

Data 

At the time this assessment began, the ambient monitoring data from 2005-2007, but not 

from 2008, had been certified as accurate and complete by the state/local monitoring agencies 

that collected them, and the data had been extensively summarized and presented in the first draft 

ISA. The EPA staff aimed to develop estimates of daylight hours PM10 light extinction for a 

reasonably representative number of days in each year of 2005-2007, to allow the application of 

statistical forms based on three years of data. However, as explained in more detail below, in 

several study areas the limited availability of starting data for these estimates resulted in estimate 

sets that do not cover all three years. Also, even in areas with some data in all three years, the 

number of days with valid estimates differs by year and is in some cases not large by typical 

standards of monitoring data completeness. 

For efficiency in the analysis, this visibility assessment uses the same 15 urban study 

areas selected for the health risk assessment. These areas are listed in Table 3-2, along with the 

area-wide (maximum) FRM-based 2005-2007 annual and 24-hour PM2.5 design values for each 

study area based on the highest-reading monitor in each area, and for the specific site used in this 

assessment.8

 (See below for an explanation of the “site-specific” columns in Table 3-2.) 

 

8

 2005-2007 PM2.5 design values were taken from the information posted at 

http://www.epa.gov/airtrends/values.html, and are consistent with the design values used in the health risk 

assessment to “roll back” current concentrations to represent achievement of alternative annual and 24-hour PM2.5 

NAAQS. Except in Dallas and Fresno, the area-wide design values are the highest design values of any monitoring 

site in the designated (1997 NAAQS) nonattainment area that has sufficiently complete data to allow the calculation 

of a design value according to the provisions of 40 CFR 50 appendix N. For Dallas, the design values come from a 

site with nearly complete data, and are somewhat higher than the highest values from a site with complete data (see 

the PM Risk Assessment, US EPA, 2010e, section 3.2.3). For Fresno, the area-wide design value is for the FresnoMadera CSA, which is only a portion of the San Joaquin Valley nonattainment area. Also, note that there are three 

cases in which the nonattainment area does not include certain areas sometimes thought of as being part of the area 

named in Table 2; monitors in these non-included areas were not considered in this assessment. (1) The design value 

shown for Pittsburgh is for the Pittsburgh-Beaver nonattainment area; the Liberty-Clairton nonattainment area is 

within the Pittsburgh CBSA but is distinct for regulatory purposes, and was not considered in this assessment. (2) 

Baltimore was treated separately, although part of a CSA with Washington DC. (3) Berks Co., PA is part of the 

Philadelphia-Camden-Vineland CSA, but not part of the Philadelphia-Wilmington nonattainment area. 
3-11

Table 3-2. Urban Visibility Assessment Study Areas 

Study Area 

Area-wide 

2005-2007 

Annual 

Design Value 

(μg/m3) 

Area-wide 

2005-2007 

24-hour 

Design Value 

(μg/m3) 

Site-specific 

2005-2007 

Annual Design 

Value 

(μg/m3) 

Site-specific 

2005-2007 

24-hour 

Design Value 

(μg/m3) 

2005 Staff Paper 

Region 

(See map in Table 

3-1) 

Tacoma 10.2 43 Same Same Northwest 

Fresno 17.4 63 Same Same Southern 

California* 

Los Angeles 19.6 55 Same Same Southern California 

Phoenix 12.6 32 7.9 15 Southwest 

Salt Lake City 11.6 55 10.7 48 Northwest 

Dallas 12.8 26 11.5 25 Southeast 

Houston 15.8 31 13.1 25 Southeast 

St. Louis 16.5 39 14.5 34 Midwest 

Birmingham 18.7 44 Same Same Southeast 

Atlanta 16.2 35 15.7 33 Southeast 

Detroit 17.2 43 Same Same Midwest 

Pittsburgh 16.5 43 15.0 40 Industrial Midwest 

Baltimore 15.6 37 14.5 35 Northeast 

Philadelphia 15.0 38 14.7 37 Northeast 

New York 15.9 42 14.4 42 Northeast 

* While not generally considered to be part of Southern California as the term is commonly used, Fresno lies just 

south of the line used in the 2005 Staff Paper (US EPA 2005) (based on earlier work by others) to separate the 

Southern California region from the Northwest region. 

For time reasons and because it was anticipated that some study areas would not contain 

more than one suitable study site, EPA staff sought to identify the single best study site in each 

area. In identifying the single best study site in each study area first consideration was given to 

the availability of collocated 24-hour data on PM2.5 and its components, because the contribution 

of PM2.5 components to PM10 light extinction will typically dominate the contribution from 

PM10-2.5. Ideally, within each study area the three types of PM2.5 data (FRM PM2.5, CSN PM2.5

components, continuous PM2.5) would be available at a common site, and that site would be 

located in a manner consistent with reliance on it to characterize visibility as it would be 

perceived by a large number of area residents and visitors. As can be seen in Table 3-2, in 10 of 

the 15 study areas the site providing FRM data for this assessment is not the area-wide design 

value site, because the area-wide design value site did not have collocated CSN and/or 

continuous PM2.5 data. 

Appendix A provides details on the site(s) identified and used in each study area, 

including information on the type of monitoring equipment that provided the data and other 

information that may help interpret the results of the analysis. A portion of this table for a single 

site – Tacoma – is presented here as Table 3-3 as an example. When viewing this document 
3-12

electronically, the site IDs in these tables are active links and can be used to view the location of 

the site via GoogleMaps.9 

In 11 of the study areas, the three types of PM2.5 data were available at a common site. In 

the remaining four areas, Phoenix, AZ, Pittsburgh, PA, Baltimore, MD, and St. Louis, MO-IL, 

two types of data were available at one site, but the remaining type of data had to be taken from 

another site and treated as being representative of the former site. 

The monitoring agencies described all but one of these sites as neighborhood or urban 

scale, indicating those agencies’ opinion that the sites represent concentrations in an area at least 

0.5 to 4 km across. An aerial view of the remaining site (in Phoenix) which did not have a scale 

characterization recorded in AQS suggests that it may be middle or neighborhood scale. As 

already stated, selected sites are not necessarily the locations of the maximum measured annual 

or 24-hour PM2.5 levels in their urban area. 

Site days which were missing 1-hour PM2.5 concentration data points for more than 25 

percent of daylight hours were excluded from the analysis, because such data gaps were judged 

to result in too much uncertainty in estimates of 1-hour PM2.5 components, 1-hour PM10 light 

extinction, and daily maximum PM10 light extinction. Days with fewer missing 1-hour PM2.5

concentration data points were retained, but no estimate of PM10 light extinction was made for 

hours without 1-hour PM2.5 concentration data (see below for more explanation). Hourly PM10-

2.5 presented more varied challenges. In four areas (Birmingham, Detroit, Baltimore, and 

Philadelphia) the site that provides the continuous PM2.5 data also hosts a continuous FEM PM10

monitor, and hourly PM10-2.5 could be calculated by difference for most hours. In other areas, 

this was not the case, and either (1) hourly instruments at two different sites were used in this 

subtraction (Tacoma, Los Angeles-South Coast Air Basin, Phoenix, St. Louis, Atlanta, and New 

York-N. New Jersey) or (2) a single regionally applicable PM10-2.5 to PM2.5 ratio calculated as 

part of the last review based on 2001-2003 24-hour FRM/FEM PM10 and PM2.5 samples was 

applied to 2005-2007 hourly PM2.5 data to estimate hourly PM10-2.5 (Fresno, Salt Lake City, 

Dallas, Houston, and Pittsburgh). In the case of Los Angeles-South Coast Air Basin, the 

continuous PM10 and PM2.5 sites were quite distant and separated by a range of hills, so the 

estimates of PM10-2.5 and its contribution to PM10 light extinction are more uncertain than if the 

monitors were clearly within the same air mass. Comments on the second review draft of this 

document from those familiar with the monitoring sites in St. Louis indicate that the site selected 

to provide continuous PM10 monitoring, though less than a mile from the site of the PM2.5 data is 

 

9

 Additional meta data on each monitoring site, and access to daily and annual data listings, can be 

conveniently obtained using GoogleEarth and the PM2.5, PM10, and CSN monitoring network KML files that can be 

downloaded from http://www.epa.gov/airexplorer/monitor_kml.htm. 
3-13

not representative of the urban area and resulted in unrealistically large PM10-2.5 values.10 

Obviously, for the five study areas for which 1-hour PM10-2.5 was estimated by application of 

ratios, PM10-2.5 estimates can only represent broad trends, not hour-specific conditions at the 

particular site. More description of the methods used for estimating hourly PM10-2.5 appears in 

section 3.3.2.

 

10 Comments concerning unrealistically high PM10-2.5 values for St. Louis are viewed as credible, but were 

received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all 

portions of this document. However, the text has been revised to caution readers with respect to the St. Louis 

results, and they will not be included in the visibility effects discussion in the final PM Policy Assessment 

document. Some graphics have been updated to exclude St. Louis results. 
3-14

Table 3-3. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Tacoma 

Study Area 

Study 

Area 

First PM2.5

Monitoring Site 

Second PM2.5

Monitoring Site (if 

applicable) 

PM10 Data Source for PM10-2.5

Tacoma 

AQS ID 530530029

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - 

L STREET 

Address: 7802 SOUTH L 

STREET, TACOMA 

0.5 miles east of I-5 

2005-2007 annual DV = 10.2 

2005-2007 24-hr DV = 43 

This is the highest 24-hour 

PM2.5 DV site in the SeattleTacoma-Olympia, WA annual 

PM2.5 nonattainment area 

Neighborhood Scale 

Parameters taken from this 

site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; 

one-in-three sampling 

schedule) 

 PM2.5 speciation (one-insix sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, 

Acceptable PM2.5 AQI & 

Speciation Mass)

Correlated Radiance 

Research M903 

Nephelometry 

No continuous PM10

monitoring at this site, see 

right hand column. 

N/A 

AQS ID 530530031

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - 

ALEXANDER AVE 

Address: 2301 ALEXANDER AVE, 

TACOMA, WA 

6.4 miles NNE of PM2.5 site 

Neighborhood Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

 Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

 Sample Analysis Method: TEOMGRAVIMETRIC 

7% of PM10-2.5 values were determined using 

regional average PM10-2.5:PM2.5 ratios from 

2005 Staff Paper

Additional Explanation 

 In this Table, the 1-hour concentration parameter “88502, Acceptable PM2.5 AQI & Speciation Mass” is the same as the ISA refers to 

as “FRM-like” PM2.5 mass. An entry of “88501, PM2.5 Raw Data” indicates that the monitoring agency makes no representation as to 

the degree of correlation with FRM PM2.5 mass. The latter type of continuous PM2.5 data were used only when the former were 

unavailable.

 Where PM10 was reported in STP, it was converted to LC before PM10-2.5 was calculated.

 For convenience, continuous PM2.5 data was obtained through the AirNow website rather than from AQS, as an initial exploration 

indicated that not all the desired 1-hour data had been submitted to AQS.
3-15 

The sampling schedule for CSN PM2.5 speciation monitoring was one-in-six days for 

Tacoma, Phoenix, Houston, Detroit, and Philadelphia, and one-in-three days for the other study 

areas. Not every scheduled CSN site day in 2005-2007 had data for all three types of PM2.5 data, 

due to missed or invalid samples. Also, for continuous PM2.5, values for a small number of hours 

of an otherwise data-sufficient day were sometimes missing, due to equipment failure or 

servicing. EPA staff retained only those days in which 75 percent or more of daylight hours had 

measurements of PM2.5 (see section 3.3. for more details). If for isolated hours at a site (or site 

pair) with collocated measurements, PM10-2.5 concentrations could not be estimated because of 

gaps in the same-hour continuous PM10 and/or PM2.5 data, EPA staff used the regional ratio 

approach described above to estimate PM10-2.5 for those specific hours. Table 3-4 provides more 

detailed information on the quarterly distribution of the successfully matched and sufficiently 

complete data available for use. As described later, for some parts of this assessment EPA staff 

substituted data for the single missing quarters of data in Phoenix and Houston, to achieve 

seasonal balance. For some sites, two CSN samplers operated on some days for data quality 

assessment purposes; when this was the case, the results from the two samplers were averaged. 

In this assessment, we have not excluded PM concentration data that may have been 

affected by exceptional events such as wildfires and wind storms. Under EPA’s Exceptional 

Events rule, for existing NAAQS states may request exclusion of such data from regulatory 

determinations, and accordingly such data are not reflected in design values for existing NAAQS 

once exclusion is approved by EPA. A similar arrangement presumably would apply to a new or 

revised secondary PM NAAQS. Design values for PM10 light extinction under current 

conditions (Table 4-2) and percentage reductions to “just meet” alternative secondary NAAQS 

based on PM10 light extinction (Table 4-3), presented below in chapter 4, may thus be 

overestimates. Overestimation is more likely for the western study sites than for the eastern 

study sites. However, PM2.5 design values shown in Table 3-2, and associated estimates of the 

reductions needed from 2005-2007 PM2.5 level to just meet alternative secondary NAAQS based 

on PM2.5 mass (Table 4-4) do reflect the exclusion of at least some data affected by exceptional 

events.
3-16 

Table 3-4. Number of Days per Quarter in Each Study Area 

Study Area Total Number 

of Days 

2005 2006 2007 

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 

Tacoma 109 0 0 0 0 13 15 15 14 12 13 14 13 

Fresno 324 19 24 27 27 30 29 29 27 26 28 30 28 

Los Angeles 300 26 28 22 28 26 26 27 22 21 26 24 24 

Phoenix 86 0 13 11 14 12 13 11 12 0 0 0 0 

Salt Lake City 306 27 28 30 26 20 28 31 20 23 25 19 29 

Dallas 274 22 24 26 22 23 23 24 24 18 23 24 21 

Houston 149 21 20 10 14 14 12 8 12 15 14 9 0 

St. Louis 292 26 27 24 27 28 19 27 28 29 25 22 10 

Birmingham 350 30 30 29 30 29 29 30 30 30 30 27 26 

Atlanta 285 20 25 25 22 26 27 26 24 25 19 26 20 

Detroit 141 12 12 10 11 12 13 11 15 11 11 12 11 

Pittsburgh 281 25 23 25 21 22 25 24 26 22 22 23 23 

Baltimore 186 19 17 15 11 15 16 18 18 12 12 17 16 

Philadelphia 145 15 11 13 10 9 13 10 13 13 14 12 12 

New York 227 22 23 13 14 23 19 18 21 19 15 19 21 

Note: Only days with matched and sufficiently complete data were retained in the assessment.

3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data 

Because systematic monitoring data on hourly PM2.5 component concentrations are not 

available for most of the 15 study areas, EPA staff extracted and applied certain information 

from the modeling platform for calendar year 2004 described in section 3.7.1.2 of the ISA, in 

which the global-scale circulation model GEOS-Chem was paired with the regional scale air 

quality model CMAQ.11 The main use of this platform in the ISA is to estimate policy-relevant 

background concentrations of PM2.5. For the urban-focused visibility assessment described here, 

however, we used results from the validation run of the platform, in which emissions for all 

emission source types and countries are included, to develop realistic diurnal variations of the 

major PM2.5 components. 

EPA staff identified the one or more 36 km-by-36 km CMAQ grid cells generally 

corresponding to the urbanized area surrounding each study site, thus omitting grid cells 

dominated by rural land uses.12 We then extracted from the detailed model output for these grid 

 

11 Similar modeling was not available for 2005, 2006, or 2007. 

12 Urbanized area here refers to a specific land area identified by the U.S. Census Bureau based on 

population density and other factors. Shape files for these areas were compared to the CMAQ grid to identify the 

grid cells to be used. 
3-17 

cells the day/hour-specific concentrations of sulfate, nitrate, elemental carbon, organic carbon, 

and “crustal/unspeciated” PM2.5 during 2004, and then we averaged across grid cells and then 

across days within the month for each individual hour of the day.13 Thus, for each species, we 

obtained 24 hour-of-day values for a month, for each of the 12 calendar months. We then 

averaged the 24 hour-of-day values in each monthly set for each component to obtain the 

corresponding 24-hour average concentration for the month. We then divided each hour-of-day 

value by the 24-hour value, to obtain a normalized diurnal profile for the pollutant, which was 

taken as the initial representation of all days in that month for 2005, 2006, and 2007 (but further 

adjusted day-by-day in a later step). In total, this resulted in 5 (components) x 12 (months) x 15 

(study areas) = 900 profiles. Visual examination of a number of these showed them to be 

reasonably smooth and generally to show morning (and sometimes also late afternoon/evening) 

peaks which are the anticipated effect of higher vehicle traffic and lower mixing heights. The 

peaks were generally moderate, as would be expected in light of the averaging of predictions for 

multiple large grid cells, the averaging across days, and the generally moderate diurnal profiles 

for SMOKE pre-processing of emissions in the CMAQ modeling platform. (Note, however, that 

as described below a later step in the estimation process reduces the smoothness in the diurnal 

pattern of PM components.) Sulfate, as would be expected for a regionally transported pollutant, 

generally had a flatter diurnal profile than for other components. Hourly nitrate concentrations 

were low when expected: during warmer months and in warmer areas. Figure 3-4 shows 

example diurnal profiles for the five PM2.5 components, for the Detroit study area for the months 

of January and August. Diurnal profiles like these were applied to 24-hour CSN measurements 

of component concentrations, as explained in detail below. 

 

13 For several of the listed components that are not direct CMAQ outputs, concentrations were estimated by 

post-processing to aggregate the appropriate CMAQ outputs. The “crustal/unspeciated” CMAQ output results from 

non-reactive dispersion of that portion of the PM2.5 emission inputs not assigned during SMOKE processing to a 

more specific CMAQ species, and is considered in most EPA analyses to represent the same material as the “soil” 

component reported for IMPROVE sampling.
3-18 

Figure 3-4. January and August Monthly Average Diurnal Profiles of PM2.5 Components 

Derived From the 2004 CMAQ Modeling Platform, for the Detroit Study 

Area.

CMAQ Relative Patterns (JANUARY)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

soil

ec

no3

so4

ocm

CMAQ Relative Patterns (AUGUST)

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

soil

ec

no3

so4

ocm
3-19 

3.2.3 Use of Original IMPROVE Algorithm to Estimate PM10 Light Extinction 

The EPA staff used the original IMPROVE light extinction algorithm, rather than the 

more recent revised version, because the original version is considered more representative of 

urban situations, when emissions are still fresh rather than aged as at remote IMPROVE sites.14 

To maintain consistency with the form of the candidate protection levels (CPLs) for PM10 light 

extinction identified in chapter 2, EPA staff excluded from the IMPROVE algorithm for total 

light extinction the term for Rayleigh scattering by gases in clean air. The formula for PM10 light 

extinction using the traditional IMPROVE algorithm but without the Rayleigh scattering term is 

shown below.

bextPM = 3 x f(RH) x [Sulfate] 

+ 3 x f (RH) x [Nitrate] 

+ 4 x [Organic Mass] 

+10 x [Elemental Carbon] 

+ 1 x {Fine Soil] 

+ 0.6 x [Coarse Mass] 

PM10 light extinction (bextPM) is in units of Mm-1, the mass concentrations of the 

components indicated in brackets are in μg/m3, and f(RH) is the unitless water growth term that 

depends on relative humidity. We refer to the first five terms in this algorithm as the five PM2.5

components. In this algorithm, the sulfate and nitrate components are to be expressed as fully 

neutralized and as retained and measured in the IMPROVE sampling and laboratory methods. 

Associated water is to be omitted from all bracketed terms since the water absorption effect is 

reflected in the f(RH) term. The organic mass component is to include the mass of associated 

elements in addition to carbon. As described below, we included steps in our development of 

estimates of hourly component concentration to ensure consistency with these aspects of the 

IMPROVE algorithm. 

3.3 DETAILED STEPS 

3.3.1 Hourly PM2.5 Component Concentrations 

The task of estimating hourly PM2.5 component concentrations is in a sense overdetermined, given the four types of available information: 24-hour PM2.5 mass by filter-based 

FRM, 24-hour component concentrations by CSN, hourly PM2.5 mass by continuous instrument, 

 

14 Other differences between the original and revised algorithms include estimates of sea salt contributions 

which can be important for near-coastal locations, inclusion of site-elevation specific Rayleigh light scattering and 

provision for calculating NO2 light absorption when NO2 data are available. Their exclusion in this assessment is 

not expected to make any appreciable difference to the results or conclusions. 
3-20 

and diurnal profiles of components from the 2004 CMAQ run. There are multiple ways in which 

two or three of these four data sources could be used to estimate hourly PM2.5 component 

concentrations, and the result generally can be expected to be at least somewhat inconsistent with 

the information in the remaining data source(s). For example, each 24-hour PM2.5 component 

mass from CSN sampling can be apportioned to hours based on the monthly average diurnal 

profile developed from the 2004 CMAQ run, but then in general the hourly values of PM2.5 mass 

determined by summing the components in an hour would not exactly match the data from the 

continuous PM2.5 instrument. EPA staff therefore used a sequence of steps which achieves a 

prioritized compromise among the data sources. In this sequence, we have given greater weight 

to the 24-hour FRM, CSN, and continuous PM2.5 mass data because these are instrument-based 

and location- and day-specific, than to the CMAQ-based profiles which are CTM-based, 

averaged to the month, and extrapolated from 2004 to each of 2005, 2006, and 2007. 

Because of differences in filter materials, sample collection, laboratory analysis, and data 

reporting, there are differences between the contribution of some PM components to PM2.5 mass 

as reported by a filter-based 24-hour FRM sampler, and the mass of the same components as 

reported by CSN (or IMPROVE) sampling. The following summary of these differences may be 

helpful in understanding the steps used to develop estimates of hourly PM2.5 components in this 

analysis. In the IMPROVE algorithm for reconstructing PM10 light extinction, the light 

extinction contribution multipliers per unit of mass concentration of components are not all the 

same for the five principal components. Consequently, care is required to estimate these 

components as consistently as possible with the IMPROVE sampling and analytical methods so 

that particle mass is correctly assigned to the right component. 

 Nitrate: CSN (and IMPROVE) sampling uses a Nylon filter for purposes of nitrate ion 

quantification, while FRM sampling uses a Teflon filter for PM2.5 mass as a whole. The 

Nylon filter limits the loss of nitrate in the form of nitric acid vapor which could 

otherwise occur if the filter temperature rises above the temperature at the time of 

collection, compared to the Teflon filter. The fine particle nitrate ion collected on nylon 

and Teflon filters are assumed to be associated with ammonium ions, and for this analysis 

ammonium is assumed to evaporate at the same rate as nitrate on the FRM filters15. 

Hence, the nitrate ion and calculated ammonium nitrate concentrations reported by CSN 

(and IMPROVE) sampling typically will be higher than the nitrate contribution to FRM 

PM2.5 mass, particularly under warm ambient conditions. On the other hand, FRM 

sampling may result in some water that is associated with nitrate being included in the 

reported PM2.5 mass, while the nitrate mass reported by CSN (or IMPROVE) sampling 

excludes all water. Continuous PM2.5 samplers employ a variety of methods for 

measuring PM2.5 mass, with correspondingly different behaviors regarding retention/loss 

 

15 EPA staff recognizes that fine particle nitrate may be in the form of calcium or sodium nitrate, but like 

the IMPROVE program treats nitrate as ammonium nitrate. 
3-21 

of nitrate. In this assessment’s approach to estimating actual ambient concentrations and 

PM10 light extinction, the FRM measurement of nitrate is used in the calculation of the 

concentration of organic carbonaceous material, but not in estimating ambient 

concentrations of nitrate or PM10 light extinction. The CSN-reported nitrate ion 

concentration and corresponding ammonium nitrate mass is used for the latter purposes. 

 Sulfate: Unlike nitrate, sulfate is not subject to loss once collected by a filter, so the 

sulfate ion mass reported by a CSN (or IMPROVE) sampler will be about the same as the 

contribution of sulfate ion to the mass reported by FRM sampling. In FRM sampling, 

sulfate ion may not be fully neutralized. When IMPROVE data are used to estimate 

PM10 light extinction, it is assumed that sulfate ion is fully neutralized. Even more 

important than nitrate, FRM sampling results in water that is associated with sulfate being 

included in the reported PM2.5 mass. While the water associated with the measured 

sulfate ion is used in the calculation of the concentration of organic carbonaceous 

material, it is not used in estimating ambient concentrations of sulfate or PM10 light 

extinction. 

 Elemental and Organic Carbon: Only the mass of carbon atoms is included in the 

reported elemental carbon and organic carbon for a CSN (or IMPROVE) sampler. In 

addition, the assignment of carbon atoms between the reported elemental and organic 

amounts is dependent on the specifics of the two different thermo-optical analytical 

methods used in the CSN vs. the IMPROVE network.16 Also, the quartz filter used to 

quantify carbonaceous material in CSN and IMPROVE sampling both absorbs and loses 

organic vapors during sampling, while the Teflon filter in a FRM sampler does not 

absorb organic vapors (although PM on the filter may do so). Therefore, some method 

other than direct measurement must be used to estimate the total mass concentration of 

organic carbonaceous material in ambient air. The IMPROVE program adjusts for 

absorption of vapors by subtracting a monthly average backup filter value, and then 

applies a standard adjustment factor (1.4 in the original IMPROVE method) to the 

remaining organic carbon measurement to estimate organic carbonaceous material. In 

contrast, the standard reports from CSN sampling submitted to AQS do not include these 

two adjustments, but it is routine for EPA staff to apply adjustments for the same 

purpose, after reporting of CSN data to AQS. The latter are based on network-wide filter 

field blanks and are judged as very approximate. For this assessment, the SANDWICH 

approach to such adjustments (Frank, 2006) is used to estimate the organic mass through 

a material balance of components measured on the CSN and FRM samplers. 

 Hourly PM2.5: The continuous instruments used for measuring hourly PM2.5 mass were 

different among sites (as listed in Appendix A). None of the instrument types that 

provided hourly data for this assessment, when averaged over 24 hours, exactly matches 

either the measurement of PM2.5 mass from a FRM sampler or the sum-of-components 

reportable from CSN sampling. Differences can arise because of differences in water 

capture and retention, inconsistent absorption and loss of organic vapors and nitric acid 

 

16 While CSN carbon sampling and analysis methods have recently been harmonized with IMPROVE 

methods at many CSN sites, it was not until mid-2007 that the first 57 sites were using the harmonized methods. 

Consequently, most of the elemental and organic carbon data used in this assessment were obtained with the original 

CSN methods. 
3-22 

vapor, etc. Furthermore, comparability between hourly and 24-hour integrated 

measurements can only be made on a daily average basis. Consequently, the continuous 

instruments providing data to this assessment can be assumed to have a range of 

correlation performance versus the FRM. In light of these consistency issues, the hourly 

data from the continuous instruments were taken to be most indicative of the relative 

concentrations of PM2.5 from hour-to-hour, with less reliance on the absolute accuracy of 

the continuous instruments.17

Taking into consideration the above information, EPA staff combined the four types of 

available PM2.5 data in each study area using the following steps. Figure 3-5 provides a flow 

chart to assist in understanding these steps. 

1. The SANDWICH method (Frank, 2006) was used to subdivide the 24-hour PM2.5 mass 

reported by the FRM for each day and site into sulfate (including associated ammonium 

and residual water during filter equilibration and weighing), nitrate (including associated 

ammonium, but not necessarily enough to fully neutralize the sulfate ion, and residual 

water during filter weighing), elemental carbon, organic carbonaceous mass, and fine 

soil/crustal mass. This is done using information from the CSN measurements, physical 

models, and day-specific temperatures. The primary purpose of this SANDWICH step is 

to estimate organic carbonaceous mass. Significantly, in the SANDWICH method, the 

component referred to as organic carbonaceous mass is actually a residual whose value is 

determined as the difference between the PM2.5 mass determined from weighing the FRM 

filter and the sum of the estimated masses of the other four mass components as listed 

above. Therefore, it is not necessary to adjust for organic carbon sampling artifacts or to 

apply the 1.4 factor commonly used to estimate organic carbonaceous material from 

IMPROVE measurements of organic carbon. The SANDWICH procedure did not 

consider sea salt in the material balance, since this is generally a very small mass 

constituent for the urban areas considered in this analysis. For the same reason, sea salt 

was also not considered in the aerosol based light extinction algorithm.18 

 

17 In 2006, EPA developed and promulgated criteria for approval of continuous PM2.5 samplers as “federal 

equivalent methods”. These criteria assure a minimum level of correlation between approved continuous 

instruments and the FRM method, when data from both are expressed as 24-hour average concentrations. However, 

in 2005-2007 no commercially available instruments were yet approved under those criteria. 18 After completion of the second draft UFVA, an error in the execution of the SANDWICH method was 

discovered. The error had the effect of reducing the estimate of nitrate on the FRM filter for some sample days, 

generally by no more than 3 or 4 μg/m3

, and increasing the estimate of organic carbon mass on the FRM filter by the 

same amount. The effect on estimates for PM10 light extinction was that light extinction had been overestimated for 

some hours in some cities. The error has been corrected in this final version. The effect of the correction is in most 

cases very small and visually imperceptible in graphics such as box plots. 
3-23 

Figure 3-5. Sequence of Steps Used to Estimate Hourly PM2.5 Components and PM10

Light Extinction 

2. The CMAQ-derived monthly diurnal profiles for the sulfate, nitrate, elemental carbon, 

organic carbon and fine soil/crustal components, like the examples for Detroit in Figure 

3-4, were multiplied by the day-specific SANDWICH-based estimates of the 24-hour 

average concentrations of these five PM2.5 components, to get day-specific hourly 

estimates of these five components (including ammonium and water associated with 

sulfate and nitrate ion). 

3. The hourly concentrations of these five components (including ammonium and water 

associated with sulfate and nitrate ion when the filter is weighed) were added together, to 

get a sum-of-components estimate of hourly PM2.5 mass for the day of the FRM 

sampling. 
3-24 

4. The hourly data from the continuous PM2.5 instrument on the day of the FRM sampling 

were normalized by their 24-hour average, to get a diurnal profile. (Recall that days were 

not used in this assessment if hourly PM2.5 mass data were missing for more than 25 

percent of daylight hours.) This profile was applied to the 24-hour PM2.5 mass reported 

by the FRM sampler, to get a preliminary, FRM-consistent estimate of hourly PM2.5 mass 

for the day of the FRM sampling. This is straightforward when all 24 values of 1-hour 

PM2.5 mass were available for the day. However, for some (but not many) days, some 

values for continuously measured hourly PM2.5 mass were missing. In such cases, EPA 

staff used only the hours with valid 1-hour PM2.5 mass values to develop the diurnal 

profile and then applied the profile to the FRM value as just described. This keeps the 

average of the valid 1-hour PM2.5 values equal to the 24-hour value from the FRM 

sampler. 

5. The two estimates of hourly PM2.5 mass from steps 3 and 4 were compared, hour-byhour. By virtue of the way they were derived, the averages of these estimates across all 

24 hours of the day will necessarily be the same (and will be equal to the 24-hour FRM 

measurement). However, while the diurnal pattern of these two estimates of the same 

physical parameter should also be generally similar, it can be expected (and it is 

observed) that the hourly measurements from the continuous PM2.5 instruments (after 

adjustment to be consistent with the FRM data) have more hour-to-hour variability. 

Figure 3-6 gives an example of this comparison, for one day for the Detroit study area. 
3-25 

Figure 3-6. Example from Detroit Study Area. 

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Concentration, ug/m3

FRM adjusted Hourly PM2.5

Sum of diurnalized SANDWICHED species data using CMAQ profiles

Example comparison from the Detroit study area of hourly PM2.5 mass on March 24, 2006 as 

estimated by applying CMAQ-based diurnal profiles to SANDWICH estimates of 24-hour 

component concentrations versus applying a diurnal profile derived from continuous PM2.5

measurements to FRM PM2.5 mass. 
3-26 

6. Given that the continuous instrument is reacting to hour-specific local conditions that can 

vary from hour-to-hour due to real variations in local emissions and dispersion/transport 

conditions, while the CMAQ-based estimates contain much less specific information, the 

diurnal pattern of PM2.5 mass observed by the continuous instrument (adjusted to be 

consistent with the FRM value for 24-hour average PM2.5) was taken as more reliable. 

Within each hour, the estimates of all five components from step 2 were increased or 

decreased by a common percentage (referred to below as Ai where the subscript i

indicates the hour) so that the sum of the five components after this adjustment was equal 

to the estimate of the hourly PM2.5 mass from step 4. The adjustment percentage varied 

from hour-to-hour. Necessarily, in some hours the adjustment is an increase in the 

concentrations of all components, and in other hours it is a decrease. While this 

adjustment preserves the consistency between the 24 values of hourly PM2.5 mass and the 

24-hour FRM mass, it can disturb the consistency between the daily average of hourly 

estimates of PM2.5 components and the SANDWICH-based estimates of 24-hour average 

component concentrations. This disturbance was generally small, because the 

adjustments necessarily go in one direction for some hours and the other direction for 

other hours. For example, for the particular day in Detroit used for illustration purposes 

in Figure 6, the effect of this step was to cause a discrepancy of 3 percent between the 

SANDWICH-based values of 24-hour sulfate concentration and the average of the 24 

estimates of 1-hour sulfate concentrations (the positive percent indicates a higher 

concentration in the result of this step than the SANDWICH-based value). The 

discrepancies were 1, 1, 2, and 2 percent for nitrate, elemental carbon, organic carbon, 

and fine soil/crustal, respectively.

7. Each hourly estimate of sulfate concentration from step 6 (which includes estimates of 

associated ammonium and particle bound water) was adjusted so that it excludes water 

and reflects full neutralization and therefore is consistent with the reporting practices of 

the IMPROVE program and the IMPROVE algorithm. This was done via these substeps: 

a. The 24-hour CSN value for the dry mass of sulfate ion (not SANDWICHed, no 

ammonium or water) was multiplied by 1.375 to reflect an assumption of full 

neutralization of dry sulfate mass.19

b. The ratio of this fully neutralized 24-hour sulfate mass to the SANDWICH-based 

24-hour sulfate value was calculated. 

c. This ratio was applied to each individual hour’s sulfate concentration from step 6. 

As in Step 6, it is possible for the 24 final hourly sulfate estimates to no longer be 

exactly consistent with the 24-hour CSN sulfate measurement, both reported as fully 

neutralized sulfate ion. 

 

19 While it would have been possible to develop a more realistic estimate of partially neutralized sulfate, the 

assumption of full neutralization was used to maintain consistency with the basis for the f(RH) term in the 

IMPROVE algorithm. 
3-27 

8. A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate 

concentration from step 6, so that the estimate of hourly nitrate would reflect actual 

atmospheric conditions and be consistent with the IMPROVE algorithm. However, the 

ratio approach used in step 7(b) for sulfate could not be applied for nitrate, so this 

adjustment had to be more complicated. Because in warm weather the FRM Teflon filter 

does not retain nitrate, the initial FRM-consistent nitrate estimate derived by applying the 

SANDWICH method to the FRM and CSN data can be zero. Such a zero value makes it 

impossible to use the ratio approach in 7(b). Instead, the adjustment was made as 

follows: 

a. The 24-hour CSN value for nitrate ion (not SANDWICHed, no ammonium or 

water) was multiplied by 1.29 to reflect an assumption of full neutralization by 

ammonia. 

b. This 24-hour value was then diurnalized using the CMAQ-based profile, similar 

to step 2. 

c. Each resulting hourly value of nitrate was further multiplied by the Ai factor from 

step 6. 

d. This new estimate of hourly nitrate was used to replace the initial nitrate value 

that had resulted from step 6. 

For cooler areas and days in which the 24-hour SANDWICH results include some nitrate, 

the effect of these steps for nitrate are exactly the same as the effects of step 7 for sulfate 

(except for the 1.29 vs. 1.375 neutralization factor). For warmer areas and days in which 

the 24-hour SANDWICH results did not include any nitrate even though nitrate was 

measured on the CSN Nylon filter, the effect of these steps is to assign the CSN nitrate to 

each hour using a combination of the information in the CMAQ-based profiles and the 

information provided by the continuous PM2.5 sampler. As in Step 6, it is possible for the 

24 final hourly nitrate estimates to no longer be exactly consistent with the 24-hour CSN 

nitrate measurement. 

The net effect of these steps is believed by EPA staff to result in hourly PM10 light 

extinction estimates with the following features with respect to some of the complicating aspects 

of PM sampling: 

 The 24-hour average of the hourly nitrate concentrations used to estimate hourly PM10

light extinction agrees closely but not exactly with the 24-hour value provided by the 

CSN sampling, and generally is higher than the contribution of nitrate to the FRM 

measure of PM2.5 mass. In some mid-day hours in some areas, estimated hourly nitrate is 

zero which is a more realistic approach than applying a 24-hour species mix to each hour. 
3-28 

 The 24-hour average of the hourly organic carbonaceous material concentrations used to 

estimate hourly PM10 light extinction achieves FRM mass balance closure, taking into 

account also the difference in nitrate and the possibly partial neutralization of sulfate ion 

on the FRM filter. Because the Teflon filter used in FRM sampling is less subject to 

positive artifacts for organic material, this approach sidesteps an area of uncertainty in the 

IMPROVE sampling method. By relying on mass closure as the driving principle for 

estimating organic material, it is not necessary to choose a multiplier to relate organic 

carbon to organic carbonaceous material.20

 The 24-hour average of the hourly elemental carbon concentrations used to estimate 

hourly PM10 light extinction agrees closely but not exactly with the 24-hour value 

provided by the CSN sampling, and with the contribution of elemental carbon to the 

FRM measure of PM2.5 mass. Elemental carbon is generally defined by the thermal 

optical transmission method used in CSN, rather than the thermal optical reflectance 

method used by the IMPROVE network. 

3.3.2 Hourly PM10-2.5 Concentrations 

Three different paths were used to estimate hourly PM10-2.5 concentrations depending on 

data availability, in the following order of preference: 

1. When hourly data from a collocated PM10 instruments were available at the continuous 

PM2.5 site in a study area, PM2.5 was subtracted hour-by-hour from PM10. Negative 

values were reset to zero. This was the approach most often used in Birmingham, 

Detroit, Baltimore, and Philadelphia. This method should result in reliable estimates of 

actual PM10-2.5 at the study site. (How well the study site represents the study area 

generally, or the most visibility-impacted portions of the study area, is a separate issue.) 

2. When collocated continuous PM10 data were not available at the continuous PM2.5 site in 

a study area, but continuous PM10 data were available at another site in or near the same 

study area, PM10-2.5 was estimated by subtraction, implicitly assuming that the latter site 

was also representative of PM10 at the former site. This was the approach most often 

used in Los Angeles, Phoenix, St. Louis, Atlanta, and New York. As a result, estimates 

of PM10-2.5 for these areas could be affected by site-to-site differences. In particular, the 

two sites in Los Angeles were a good distance apart, and the PM10 site in Victorville may 

represent influences from agricultural operations rather than typical urban influences. In 

St. Louis, the PM10 site may also have been influenced by particular local sources. In 

both cases, very high estimates of hourly PM10-2.5 may not represent reality at the PM2.5

site, although they may be reasonable estimates for the PM10 site. 

 

20 In other work, EPA staff has observed that when applied to urban sampling data together with CSN 

network-wide field blanks applied to reported OC measured concentrations, the multipliers that can be backcalculated from the results of the SANDWICH method tend to be nearer to 1.4 than to the higher value used in the 

new IMPROVE algorithm. 
3-29 

3. If neither of the first two methods was possible, a regional average ratio of PM10-2.5 to 

PM2.5 determined from an analysis of 24-hour data for the 2005 Staff Paper was applied 

to hourly PM2.5 from the continuous instrument associated with the study area. This was 

the approach used for all hours in Tacoma, Fresno, Salt Lake City, Dallas, Houston, and 

Pittsburgh. With this approach, it is not possible for there to be any particularly high 

estimates of hourly PM10-2.5. 

The estimation of PM10-2.5 was further complicated because some types of data were 

missing for isolated hours in the 2005-2007 period. As result, even for a single study area more 

than one method sometimes had to be used to estimate hourly PM10-2.5. Appendix A gives more 

specifics about the estimation of hourly PM10-2.5 in each study area. 

The three-path approach described here is similar to that used for the visibility analysis 

reported in the 2005 Staff Paper. While the second and third paths involve the use of data and 

assumptions that are not robust compared to the use of paired, collocated, same-method 

continuous instruments or compared to the use of paired low-volume filter-based samplers, in 

most areas and periods the contribution to PM10 light extinction from the resulting PM10-2.5

concentrations was not large compared to the PM10 light extinction contribution from PM2.5

components. 

3.3.3 Hourly Relative Humidity Data 

Hourly relative humidity (RH) data for each study area’s primary monitoring site were 

obtained hour-by-hour from the closest available non-missing relative humidity measurement, as 

reported by either an air monitoring station reporting such data to AQS or a National Weather 

Service (NWS) station. For the AQS RH data, parameter 62201 values were utilized. RH data 

from both sources are expressed as percentages. 

3.3.4 Calculation of Daylight 1-Hour PM10 Light Extinction 

Because the interest in this analysis is on visibility during daylight hours, EPA staff 

applied a scheme to denote those hours that would be considered daylight hours. For simplicity, 

all the days within each “season” in all study areas were considered to have the same daylight 

hours.21 Table 3-5 shows the dividing times used to denote daylight hours for the study areas. 

Unless otherwise stated, all subsequent discussion of the results refers only to the values of 

parameters during these daylight hours. 

The original IMPROVE algorithm was applied hour-by-hour to estimate PM10 light 

extinction in each study area for each daylight hour. When doing so, we capped the value of the 

 

21 This simple approach does not account for the effects of the actual date within a three-month season, 

latitude, or east-west position within a time zone on the actual local hours that are entirely daylight. Appendix I 

examines the possible impact of this simplification, concluding that it is unlikely to affect later answers to policy 

relevant questions. 
3-30 

humidity adjustment factor in the IMPROVE algorithm (“f(RH)”) at the value of 7.4 that it has 

for a relative humidity of 95 percent. The effect of measurement errors in relative humidity at 

values above 95 percent on the value of f(RH) and thus on reconstructed PM10 light extinction is 

considerable because of the highly nonlinear form of the function in that range. This creates 

uncertainty as to the representativeness of the extinction values calculated with high values of 

relative humidity.22 . 

Table 3-5. Assumed Daylight Hours by Season (Local Standard Time) 

NovemberJanuary February-April May-July August-October 

First hour that is 

entirely daylight 8:00-9:00 AM 7:00-8:00 AM 5:00-6:00 AM 6:00-7:00 AM 

Last hour that is 

entirely daylight 3:00-4:00 PM 5:00-6:00 PM 6:00-7:00 PM 5:00-6:00 PM 

Number of 

daylight hours 8 11 14 12 

3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from 

PM10 Light Extinction NAAQS Scenarios and Most Results 

As advised by CASAC as part of its comments on the first public review draft of this 

assessment, EPA staff considered whether to structure the PM10 light extinction NAAQS 

scenarios so that ambient data obtained during daylight hours in which relative humidity was 

greater than 90 percent would play no role in the form of the NAAQS, i.e., so that those data 

would not enter into the calculation of the design value. EPA staff obtained hourly 

meteorological parameters from NWS monitoring sites near the 15 study sites (usually a major 

airport), for 2005 through 2007, for all days in this period including days for which PM 

observations to support estimates of PM10 light extinction are not available. Using these data, 

EPA staff compared the occurrence of liquid precipitation, hail, other frozen precipitation, fog, 

mist, and smoke/haze during daylight hours with humidity greater than 90 percent and during all 

other daylight hours. 23 The first five of these conditions are generally considered natural causes 

 

22 The IMPROVE program also caps the value of f(RH) at its value for a relative humidity of 95% when 

reporting visibility in deciviews. 23 The “smoke/haze” category is not an original NWS reporting category. It is a combination of two 

original NWS weather categories: smoke and haze. The explanation of these categories in the NWS documentation 

does not allow EPA staff to be confident that these terms have distinct and clear meanings that are uniformly applied 

across observation sites, so they have been combined in this presentation. As best EPA staff can determine, the 

combined category reflects some mix of smoke from burning biomass, smoke from industrial processes, dust from 
3-31 

of reduced visibility. Table 3-6 presents this comparison.24 The percentages of hours with each 

of these five conditions individually are shown for the two sets of daylight hours.

 wind storms, volcanic ash, and general urban haze. Also, the reported conditions may be at some distance from the 

observation site. 24 Compared to the version of this table in the second external review draft, this version correctly omits 

non-daylight hours which were inadvertently included in the earlier version, reports results for four study areas for 

which results were missing in the earlier version, and separates “mist” from “smoke/haze” to reflect that “mist” is a 

natural condition while “smoke/haze” is not always a natural condition. 
3-32 

Table 3-6. Comparison of Meteorological Parameters for Daylight Hours with Relative Humidity Greater 

Other Daylight Hours, During 2005 -2007 

Study Area 

Daylight Hours with Relative Humidity <= 90% Daylight Hours with Relative H

Number 

of 

Hours 

Percentage of Hours with Weather or Other 

Condition Number 

of 

Hours 

Percentage of Hours with Wea

Liquid 

Precip. 

Hail 

Other 

Frozen Precip. 

Fog Mist

Smoke/

Haze Any Liquid Precip. Hail

Other 

Frozen Precip. 

Fog 

Tacoma 10,326 12% 0% 0% 0% 0% 4% 14% 1,756 36% 0% 1% 10% Fresno 11,758 3% 0% 0% 1% 0% 15% 17% 342 25% 0% 1% 60% Los Angeles 11,419 2% 0% 0% 0% 0% 8% 9% 713 25% 0% 0% 12% Phoenix 12,123 1% 0% 0% 0% 0% 0% 1% 43 67% 0% 0% 30% Salt Lake City 11,810 4% 0% 2% 1% 0% 4% 8% 304 28% 0% 40% 42% Dallas 11,827 4% 0% 0% 1% 0% 5% 8% 223 68% 0% 2% 20% Houston 11,525 6% 0% 0% 1% 0% 6% 9% 645 42% 0% 0% 25% St. Louis 11,590 5% 0% 1% 1% 0% 10% 14% 583 56% 0% 8% 48% Birmingham 11,590 5% 0% 0% 1% 0% 9% 11% 486 56% 0% 0% 41% Atlanta 11,337 5% 0% 0% 1% 0% 10% 13% 867 50% 0% 0% 45% Detroit 11,484 5% 0% 3% 1% 0% 9% 14% 676 51% 0% 16% 39% Pittsburgh 10,603 5% 0% 3% 1% 0% 9% 14% 1,261 46% 0% 9% 12% Baltimore 11,321 4% 0% 1% 2% 0% 12% 14% 858 53% 0% 5% 38% Philadelphia 11,125 4% 0% 1% 1% 0% 8% 11% 878 47% 0% 3% 33% New York 11,799 7% 0% 1% 1% 0% 10% 14% 397 66% 0% 8% 48% Average 11,442 5% 0% 1% 1% 0% 8% 11% 669 48% 0% 6% 34% 
3-33 

NWS observations of these conditions are instantaneous, and are generally made about 

50 minutes after the hour. The relative humidity observations are made at the same time. It 

should be noted that this analysis of the co-occurrence of high relative humidity and these five 

conditions uses data from NWS sites other than the AQS sites that provided the data used to 

estimate PM10 light extinction. AQS sites could not be used for this analysis because they 

generally do not report similar weather condition data. 

The comparison for the 15 sites shows that in the set of hours with relative humidity 

above 90 percent, the frequencies of liquid precipitation (rain), other frozen precipitation (snow 

and sleet), or fog ranged as high as 68 percent, and were considerably higher for the same 

condition than in the set of hours with lower relative humidity. The frequencies of hail and mist 

were all less than 0.5 percent and thus too low for meaningful comparisons. Moreover, except in 

Tacoma, the frequency of rain or fog at the observation moments during the hours with relative 

humidity less than or equal to 90 percent was less than 8 percent. Also, a separate analysis (not 

shown) indicated that rainy hours with lower relative humidity experience considerably less 

accumulation than rainy hours with higher relative humidity. Based on this assessment, the 90% 

relative humidity cutoff criteria is effective in that on average less than 6 percent of the daylight 

hours are removed from consideration, yet those hours have on average about 10 times the 

likelihood of rain, 6 times the likelihood of snow/sleet, and 34 times the likelihood of fog 

compared to hours with 90% or less relative humidity. 

Rain, snow/sleet, and fog cause a natural reduction in visibility, independent of PM 

concentrations. To reduce the likelihood that a design value for a secondary PM NAAQS could 

be affected by measurements made under natural weather conditions that reduce visibility, for 

this assessment EPA staff eliminated from the design value definition any contribution from 

PM10 light extinction values that come from any daylight hours with relative humidity above 90 

percent.25 Also, because PM10 light extinction during such hours is not as likely to be the 

primary cause of adverse effects on the public, all figures and tables in the body of this document 

and in Appendices that present PM10 light extinction values or statistics exclude values for such 

hours (unless explicitly stated to include them), so that the patterns of PM10 light extinction 

during the remaining daylight hours can be seen clearly. Figures and tables that present PM 

component concentrations and relative humidity values are based on all daylight hours, however. 

More information on this topic can be found in Appendix G, which reports by study area 

the percentages of daylight hours that were excluded from design values, the distribution of the 

excluded hours by time of day, and the percentage of days that had one or more daylight hours 

 

25 Another consideration is that instruments used to measure light extinction could be adversely affected if 

allowed to operate without heating or other protective method (such as diffusion drying of incoming air) when 

relative humidity is very high. If protected, however, the measured light scattering would not reflect actual ambient 

conditions. 
3-34 

eliminated. Appendix G also contains box plots which contrast the distributions of daylight 1-

hour PM10 light extinction values (and maximum daily daylight 1-hour PM10 light extinction, see 

section 3.3.6) before and after this elimination step. The tile plots in Figure 3-12 also present 

additional detailed information on the specific hours that had relative humidity values above 90 

percent, and on the PM10 light extinction values during those and other daylight hours. 

3.3.6 Calculation of Daily Maximum 1-Hour PM10 Light Extinction 

Daily maximum 1-hour PM10 light extinction is a statistic of interest in this assessment, 

as briefly discussed in section 1.4.3. The daylight hour with the maximum value of PM10 light 

extinction and the corresponding PM10 light extinction value were identified for each day for 

each study area. As mentioned in section 3.2.1, days which were missing 1-hour PM2.5 values 

for more than 25 percent of daylight hours were not used in this analysis. No further 

completeness requirement for 1-hour data during a day was applied when selecting the daylight 

hour with the maximum value of PM10 light extinction. 

3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS 

3.4.1 Levels of Estimated PM2.5, PM2.5 Components, PM10-2.5, and Relative Humidity 

Figure 3-7 presents box-and-whisker plots to illustrate the distributions in each study area 

of the estimates of 1-hour PM2.5 (the diurnalized FRM value, resulting from step 4 in section 

3.4.1), PM10-2.5, and relative humidity over the entire 2005-2007 study period. In the plot for 

each parameter, areas are ordered by longitude, to make it easier to see East-versus-West 

regional differences. For these three parameters, the distributions are given for all the daylight 1-

hour estimates, including hours with relative humidity greater than 90 percent. Similar plots of 

the daily maximum daylight 1-hour values of PM2.5 and PM10-2.5 concentrations and relative 

humidity are available in Appendix B, as are plots of all daylight 1-hour values for each of the 

PM2.5 component species.26

From these plots we see that the distributions of PM2.5 generally trend toward higher 

concentrations from West to East except for the two California urban locations which have PM2.5 

concentrations more typical of eastern areas. The lowest median PM2.5 concentrations are in 

Tacoma, WA, and Phoenix, AZ. Median PM10-2.5 concentrations are highest in St. Louis, MO, 

and Phoenix, AZ, and lower elsewhere. The highest outlier PM10-2.5 concentrations are in St. 

Louis, MO, and Los Angeles, CA. Relative humidity is lowest for the western urban areas 

except for Tacoma, WA, which is similar to the northeastern urban locations with respect to 

 

26 In all box-and-whisker plots in this document, the box represents the 25th to 75th percentile range and the 

whiskers represent the 10th and 90th percentile points of the data; individual data points below the 10th percentile and 

above the 90th percentile are graphed as small circles (which may not all be visible because they may lie on top of 

one another as is the case for relative humidity in Figure 3-7(c) because relative humidity is reported as an integer). 
3-35 

Figure 3-7. Distribution of PM Parameters and Relative Humidity Across the 2005-2007 

Period, by Study Area 

(a) Estimates of 1-Hour PM2.5 Mass, Based on Applying Continuous Instrument-based 

Diurnal Profiles to 24-hour FRM PM2.5 Mass 
3-36 

Figure 3-7. Distribution of PM Parameters and Relative Humidity Across the 2005-2007 

Period, by Study Area, continued 

(b) Estimates of 1-Hour PM10-2.5 
3-37 

Figure 3-7. Distribution of PM Parameters and Relative Humidity Across the 

2005-2007 Period, by Study Area, continued 

(c) 1-Hour Relative Humidity 

humidity. These hourly daylight PM concentration and relative humidity box and whisker plots 

are consistent with our expectations based on regional 24-hour PM concentration values and 

humidity climatology 

3.4.2 Levels of Estimated PM10 Light Extinction 

Figure 3-8 presents box-and-whisker plots to illustrate the distributions of the estimates 

of daylight 1-hour reconstructed PM10 light extinction levels in each area in each year (excluding 

hours with relative humidity greater than 90 percent). The distribution of (a) the daily maximum 

1-hour values and (b) the individual 1-hour values are both shown. The horizontal dashed lines 

in the plots represent the low, middle, and high CPLs for PM10 light extinction as discussed in 

section 2.6. These benchmarks for PM10 light extinction are 64, 112, and 191 Mm-1, 

corresponding to the benchmark VAQ values of 20 dv, 25 dv and 30 dv. Table 3-7 provides (a) 

the percentages of days (across all of 2005-2007, unweighted) in which the daily maximum 
3-38 

daylight 1-hour PM10 light extinction level was greater than each of the three CPLs (excluding 

hours with relative humidity greater than 90 percent), and (b) the similar percentage based on all 

daylight hours (with the same exclusion).

As was also seen in the comparable PM2.5 concentration box and whisker plots in Figure 

3-7, the high percentile hourly PM10 light extinction values in Figure 3-8 tend to be higher in the 

eastern urban areas and lower in the non-California western urban areas. The distributions of 

maximum daily PM10 light extinction values are higher (Figure 3-8a), as expected, than for all 

hours (Figure 3-8b). Both Figure 3-8 and Table 3-7 indicate that all 15 urban areas have daily 

maximum hourly PM10 light extinctions that exceed even the highest of the CPLs some of the 

time. Again, the non-California western urban locations have the lowest frequency of maximum 

hourly PM10 light extinction with values in excess of the high CPL for 8 percent or fewer of the 

days. Except for the two Texas and the non-California western urban areas, all of the other 

urban areas exceed that high CPL from about 20 percent to over 60 percent of the days. Based 

on these estimated maximum hourly PM10 light extinction estimates, all 15 of the urban areas 

exceed the low CPL for about 40 percent to over 90 percent of the days. As noted in section 

3.2.1, in 10 of the 15 study areas the study site used in this assessment is not the site in the study 

area with the highest concentrations of PM2.5. Thus, these estimates may not characterize 

visibility in the worst-visibility portion of each study area. 

In the last review of the secondary PM NAAQS, the pattern of light extinction during the 

day was of particular interest. To illustrate the distributions of 1-hour PM10 light extinction 

levels in specific daylight hours, Figure 3-9 shows the distributions of 1-hour PM10 light 

extinction across the entire three-year study period, individually for the study areas (excluding 

hours with relative humidity greater than 90 percent). (Appendix E provides additional graphics 

related to temporal/spatial patterns of light extinction.) These plots show that high PM10 light 

extinction can occur during any of the daylight hours, though for most of these urban areas the 

morning hours have somewhat higher PM10 light extinction than in the afternoon.27 Urban areas 

without a pronounced preference for morning high PM10 light extinction include Phoenix, AZ; 

Salt Lake City, UT; Tacoma, WA; Fresno, CA; and Philadelphia, PA.

 

27 If hours with relative humidity greater than 90 percent were not eliminated, the tendency for higher PM10

light extinction in the morning hours would be stronger. 
3-39 

Figure 3-8. Distributions of Estimated Daylight 1-Hour PM10 Light Extinction and 

Maximum Daily Daylight 1-Hour PM10 Light Extinction (in Mm-1 units) 

Across the 2005-2007 Period, by Study Area (Excluding Hours 

with Relative Humidity Greater Than 90 Percent). 

(a) Maximum Daily Values 

 
3-40 

Figure 3-8. Distributions of Estimated Daylight 1-Hour PM10 Light Extinction and 

Maximum Daily Daylight 1-Hour PM10 Light Extinction (in Mm-1 units) 

Across the 2005-2007 Period, by Study Area (Excluding Hours with Relative 

Humidity Greater Than 90 Percent), continued. 

(b) Individual 1-Hour Values 
3-41 

Table 3-7. Percentage of Daily Maximum Hourly Values and Individual Hourly Values 

of Daylight PM10 Light Extinction Exceeding CPLs (Excluding Hours 

with Relative Humidity Greater Than 90 Percent). 

Study Area 

Number of Days with 

Estimates

Candidate Protection Level

64Mm-1 112 Mm-1 191 Mm-1

(a) Percentage of Daily Maximum Hourly Values 

Exceeding CPL

Tacoma 109 52 22 4 

Fresno 324 76 52 29 

Los Angeles 300 90 83 61 

Phoenix 86 42 7 1 

Salt Lake City 306 44 17 8 

Dallas 273 80 41 10 

Houston 148 79 45 11 

St. Louis 289 98 78 40 

Birmingham 349 89 64 34 

Atlanta 279 91 75 31 

Detroit 141 87 68 43 

Pittsburgh 277 85 57 26 

Baltimore 181 80 50 23 

Philadelphia 143 86 63 31 

New York 225 83 59 28 

Average 229 77 52 25 

Number of Daylight Hours 

with Estimates

(b) Percentage of Individual Daylight Hours 

Exceeding CPL

Tacoma 1087 14 4 1 

Fresno 3533 41 20 10 

Los Angeles 3048 68 42 19 

Phoenix 988 11 1 0 

Salt Lake City 3366 17 7 3 

Dallas 3043 33 10 2 

Houston 1504 35 8 1 

St. Louis 3096 66 36 11 

Birmingham 3763 57 25 8 

Atlanta 2507 60 28 5 

Detroit 1547 62 36 14 

Pittsburgh 2842 53 25 7 

Baltimore 1873 55 24 7 

Philadelphia 1468 55 28 9 

New York 2296 53 28 9 

Average 2398 45 21 7 
3-42 

Figure 3-9. Distributions of 1-Hour PM10 Light Extinction Levels by Daylight Hour Across the 2005-20

Area (Excluding Hours with Relative Humidity Greater Than 90 Percent). 
3-43 

3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity 

and PM10 Light Extinction 

Figure 3-9 shows the distribution of relative humidity values at each daylight hour, for 

each study area across 2005-2007 (excluding hours with relative humidity greater than 90 

percent).28 As expected, in every area relative humidity is lowest in the early afternoon, typically 

the warmest part of the day. Relative humidity is most similar across areas in the early 

afternoon, as observed in the 2005 Staff Paper. However, even in this period there are notable 

differences among areas. This variation was not as evident in the information presented in the 

2005 Staff Paper because only regionally averaged information was presented. In all areas, there 

is considerable variation in hour-specific relative humidity during the three-year period. 

To allow closer inspection of the relationship between PM10 light extinction values and 

relative humidity values, Figure 3-10 is a scatter plot of actual 1-hour relative humidity and 1-

hour reconstructed PM10 light extinction (excluding hours with relative humidity greater than 90 

percent). Horizontal lines are included in each of the individual plots corresponding to the three 

benchmarks for PM10 light extinction and a vertical line in each for the 90 percent relative 

humidity cutoff. There are many instances with PM10 light extinction greater than the CPLs 

when relative humidity is 90 percent or lower. Notice that in Figure 3-10 there also are plenty of 

high humidity conditions for each urban area that correspond to low PM10 light extinction values. 

This is because humid air does not by itself contribute to light extinction. Particles composed of 

material that absorbs water in high relative humidity conditions (e.g., sulfate and nitrate PM) 

swell to larger solution droplets that scatter more light than their smaller dry particle counterparts 

in a less humid environment. The magnitude of the relative humidity effect on light extinction 

depends directly on the concentration of these hygroscopic PM components. (Figure 3-10 

reveals skips in reported relative humidity values for some but not all the study areas. This is a 

result of calculations of relative humidity from dry and wet bulb temperatures reported to the 

nearest whole Celsius degree.) 

 

28 Similar information on diurnal patterns but broken out by season is given in Appendix E. 
3-44 

Figure 3-10. Distributions of 1-Hour Relative Humidity Levels by Daylight Hour (X-axis) Across the 20

Study Area (Excluding Hours with Relative Humidity Greater Than 90 Percent). 
3-45 

Figure 3-11. Scatter Plot of Daylight 1-Hour Relative Humidity (Percent) vs. Reconstructed PM10 Light 

Across the 2005-2007 Period, by Study Area (Excluding Hours with Relative Humidity Greater Tha
3-46 

3.4.4 Tile Plots of Hourly PM10 Light Extinction 

Figure 3-12 consists of “tile plots” that show the estimated levels of 1-hour PM10 light 

extinction for each daylight hour for each study area. These plots assist in understanding the 

times of the year and hours of the day in which high relative humidity and high PM10 light 

extinction occur, both separately and together. 

Time runs horizontally with each row of tiles representing a single day from midnight 

(left site) to midnight (right side), and vertically from January (top) to December (bottom). Each 

tile represents one hour of the year for which data to estimate PM10 light extinction were 

sufficient. Sites with 1:3 speciation sampling have more (and smaller) tiles than sites with 1:6 

speciation sampling. The tick marks on the vertical axis identify the first available sample day of 

each month identified by its month number. 

PM10 light extinction is presented in terms of four ranges or bins defined by the two 

intervals between the three CPLs, a bin above the high CPL, and a bin below the low CPL. For 

the hours with relative humidity of 90 percent and below (referred to as “Low RH bext” in the 

figure legend), shades of green are used to indicate the CPL range. Contrasting blue color scales 

are used for the tiles representing hours with relative humidity greater than 90 percent (referred 

to as “High RH bext” in the shading legend), so that the hours excluded from the PM NAAQS 

scenarios (see section 3.3.5 and Chapter 4) can be distinguished. Hours with missing PM2.5 data 

from the continuous instrument have no estimates of PM10 light extinction and are white. Such 

cases are rare, following the prior complete exclusion of days in which more than 25 percent of 

daylight hours were missing such data. 

Note that for Tacoma and Phoenix there are plots for only two years because the third 

year did not have suitable data, and for Phoenix and Houston only 9 months are shown for one of 

the available years because suitable data were not available for the remaining quarter (the 

available 9 months of results are stretched over the same vertical distance as the 12 months in the 

other cases). 

One observation that can be made in looking at these tile plots is that in very many cases, 

days which have one or more hours with high PM10 light extinction excluded because of high 

relative humidity have other hours with high PM10 light extinction which are not excluded. 

Although none of the PM10 light extinction NAAQS scenarios considered in Chapter 4 

are based on averaging periods longer than one hour, these tile plots can be used to get a rough 

sense of whether hours with high PM10 light extinction tend to be isolated, such that average 

values over several hours would be considerably lower, or tend to occur together, such that a 

longer averaging period would produce roughly the same design value. A number of the eastern 

urban areas have numerous day-long haze episodes throughout the year (e.g. St. Louis, Detroit, 

Pittsburgh, Philadelphia and New York) or seasonally (e.g. Fresno and Salt Lake City, in the 
3-47 

winter, and Los Angeles and Atlanta in the summer). Some of the urban areas have morning 

haze levels that diminish later in the day on a year-around basis (e.g. Dallas) or seasonally (e.g. 

Los Angeles, Birmingham and Atlanta in winter and Tacoma, Fresno, and St. Louis in the 

summer). This type of information may be useful in this regard during the subsequent 

preparation of the final Policy Assessment Document. 
3-48 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction 
3-49 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-50 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-51 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-52 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-53 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-54 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-55 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-56 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-57 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-58 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-59 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-60 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-61 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-62 

Figure 3-12. Tile Plots of Hourly PM10 Light Extinction, continued 
3-63 

3.4.5 Extinction Budgets for High PM10 Light Extinction Conditions 

An extinction budget for a single period shows the contribution that each PM component 

makes to PM10 light extinction via the additive terms of the IMPROVE algorithm. It can be 

expected that the pattern in the extinction budgets will vary by time of year and by study area. 

Examination of extinction budgets allows initial insights into what pollutants cause poor urban 

visibility and what emission reduction approach may be most effective in reducing PM10 light 

extinction. 

Figure 3-13 presents (a) day-specific maximum daylight 1-hour PM10 light extinction 

budgets for the 10 percent of the days in each study area that have the highest daily maximum 1-

hour PM10 light extinction levels (excluding hours with relative humidity greater than 90 

percent), and (b) light extinction budgets for the greatest 2 percent of all individual daylight 

hours with the same relative humidity restrictions. The day and hour of each hourly budget are 

indicated on the horizontal axis, and the hours are arranged chronologically. Note that the 

vertical scale differs from city-to-city, to accommodate the wide variation in PM10 light 

extinction values. 

Since there is an annual average of about 10 daylight hours per day 29, there are 

approximately twice the number of hours (i.e., bars in the plots) included in the top 2% of all 

daylight hours form compared to the number of hours in the top 10% of the daily maximum 1-

hour form. The rationale for pairing the top 10% of the maximum daily 1-hour PM10 light 

extinction form with the top 2% of all hours PM10 light extinction form was the similarity of the 

design values for the 90th and 98th percentiles for each form, respectively,, as discussed in 

Chapter 4 (see Table 4-2 and Figure 4-1). In each of these plots the height of the shortest bar is 

the PM10 light extinction design value associated with the selected form (e.g., the smallest value 

in the top 2% of all hours corresponds to the design value of the 98th percentile of all hours 

form). The largest light extinction value in each pair of plots is identical, representing the single 

largest daylight hour value for the city. As a result, the ranges of bar heights for each city’s pair 

of plots are approximately the same. 

The paired extinction budget plots in Figure 3-13 provide a means to examine the 

similarities and differences between the PM components that contribute to the daylight hours 

with the greatest PM10 light extinction as identified by the two forms. Though for each city there 

are twice the number of hours selected by the 2% of all hours form compared to the 10% of 

maximum daily form, the relative component contributions are generally quite similar. Much of 

the reason for this similarity of the extinction budgets between forms has to do with the 

selections of the same hours by both forms and having the additional hours for the 2% form 

 

29 Daylight hours are determined for this assessment as described in section 3.3.4 and Appendix I. 
3-64 

coming as multiple (often consecutive) hours in some of the same days that contained the top 

10% maximum daily values. These multiple hours generally have similar relative composition. 

For example notice that among the Tacoma top 2% of all hours, there are four consecutive hours 

on November 8, 2007, which includes the largest hourly daylight PM10 light extinction in the 

Tacoma dataset. 
3-65 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

 1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

 Daylight Hours 

Tacoma 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-66 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Fresno 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 

 
3-67 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Los Angeles 

 (a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-68 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Phoenix 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-69 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Salt Lake City 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-70 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Dallas 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-71 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Houston 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-72 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

St. Louis30

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 

 

30 See footnote 10 above regarding concerns with respect to the St. Louis results. 
3-73 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Birmingham 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-74 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Atlanta 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-75 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Detroit 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-76 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Baltimore 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-77 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Pittsburgh 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-78 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

Philadelphia 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-79 

Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

1-Hour PM10 Light Extinction and for the Top 2 Percent of Individual 

Daylight Hours, continued 

New York 

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM10 Light Extinction 

(b) Top 2 Percent of Individual Daylight Hours 
3-80 

Table 3-8 shows the numbers of common and unique days selected across the two forms 

as well as the numbers of days selected by each form. All of the cities have more common than 

unique day selected by the two forms. Salt Lake City has the highest fraction of unique days, so 

it is likely to have greater differences in their PM component composition selected by the two 

forms. For example, the 2% of all hours form for Salt Lake City selected only one hour of one of 

the days when the PM carbonaceous components was the major contributor compared with the 4 

hours with high PM carbonaceous components selected by the top 10% of maximum daily form 

(see Figure 3-13 for Salt Lake City). By comparison, for Salt Lake City most of the multiple 

hours in single days had high contributions from PM nitrate. The overall effects of these 

differences for Salt Lake City are more easily seen by viewing the average extinction budgets 

using the two forms by city as shown in Figure 3-14. The 2% of all daylight hours form has a 

greater contribution to light extinction by PM nitrate and a larger average light extinction than 

for the 10% of maximum daily 1-hour form. Differences between the average extinction budgets 

for the other urban areas are much smaller than for Salt Lake City. 

Table 3-8. The Numbers of Common and Unique Days Selected for Each of the 15 

Urban Areas by the Top 10% of Daily Maximum and the Top 2% of all 

Hours Form. (Also shown are the numbers of days selected for each form.) 

10% vs. 2% Number of Days 

Common Unique 10% 2% 

Tacoma 11 3 11 14 

Fresno 25 7 32 25 

Los Angeles 29 9 30 38 

Phoenix 9 6 9 14 

Salt Lake City 16 15 31 16 

Dallas 25 2 27 25 

Houston 14 11 14 23 

St. Louis 29 4 29 33 

Birmingham 35 15 35 47 

Atlanta 27 6 27 33 

Detroit 13 1 14 13 

Baltimore 13 5 18 12 

Pittsburgh 28 3 28 31 

Philadelphia 14 0 14 14 

New York City 22 1 23 23 
3-81 

Figure 3-14. Average PM10 Light Extinction Budgets for the 15 Cities for Hours in the (a) 

Top10 Percent of the Maximum Daily PM10 Light Extinction and (b) Top 2 Percent 

of all Daylight Hours of PM10 Light Extinction. 

(a) 

(b) 

The patterns of results for individual selected hours as shown in Figure 3-13 and for city 

averages as shown in Figure 3-14 are generally as expected in light of emissions and climate 

differences among study areas. Except for the PM2.5 soil component, each of the components of 

PM10 light extinction is a major contributor to extreme light extinction events at some time and 

location. In the West, carbonaceous PM2.5 (i.e., organic mass and elemental carbon), nitrate, 

and/or coarse mass (especially in Phoenix) tend to be most responsible for these high haze hours. 

In the East it tends to be sulfate, nitrate, and the carbonaceous PM2.5 components that are the 

large contributors to PM10 light extinction. From the sample period dates we can determine the 

seasonal variations in major components. Nitrate and carbonaceous PM2.5 contribute more to the 
3-82 

extreme PM10 light extinction periods during winter, while sulfate contributes more in the 

summer. In many of the more northerly eastern urban areas, a combination of sulfate and nitrate 

contributes to high PM10 light extinction year-round. 

Looking at individual urban areas, the following are some highlights: 

 Tacoma has its highest PM10 light extinction hours in the colder months and 

primarily due to carbonaceous PM2.5 components. Because coarse PM was 

estimated by applying a regional factor to the local PM2.5 mass value, it would not 

have been possible for the results to indicate a significant coarse PM contribution 

to PM10 light extinction even if one existed at this site. However, from what EPA 

staff know of the area, it is unlikely that there is a significant contribution from 

coarse PM. 

 Extreme haze hours in the two California urban areas are primarily caused by 

high nitrate PM2.5, though Los Angeles has two extreme hours associated with 

coarse PM and several other hours with moderate contribution from coarse PM. 

Recall that estimates of coarse PM in Los Angeles are based in part on hourly 

PM10 measurements in Victorville, and may not represent coarse PM at the PM2.5

mass and speciation site in Rubidoux or in the larger South Coast Basin. Also, 

such high coarse PM values may indicate influence from exceptional winds in 

Victorville. Figure B-1(b) in Appendix B shows that several other days with high 

daily maximum PM coarse concentrations had concentrations only about 60 

percent or less than on the two days appearing in Figure 3-13; the fact that these 

other days do not appear among the top 10 percent indicates that other 

contributors to PM10 light extinction were low on those days. Whether or not the 

PM10 measurements in Victorville represent the PM2.5 mass and speciation site in 

Rubidoux, it can be concluded that nitrate and to a lesser extent sulfate dominate 

PM10 light extinction on the days likely to be above the CPLs. Because coarse 

PM for Fresno was estimated by applying a regional factor to the local PM2.5 mass 

value, it would not have been possible for the results to indicate a significant 

coarse PM contribution to PM10 light extinction even if one existed at the Fresno 

site. However, given the presence of agricultural operations and occasional high 

winds in the San Joaquin Valley, the possibility of a significant contribution from 

coarse PM in some hours cannot not be ruled out. 

 Phoenix is unique among the 15 urban areas in having most of its extreme PM10 

light extinction caused by coarse PM, though there are a few top-10-percent days 

where the maximum hourly haze is dominated by carbonaceous, sulfate, and 

nitrate PM2.5. Unlike for Los Angeles, this domination by coarse PM is no doubt 
3-83 

correct. PM10 measurements for Phoenix come from a site near the center of the 

metro area, while the PM2.5 measurements are from a more peripheral site (see 

Appendix A) and are probably underestimates of PM2.5 at the PM10 measurement 

site; this would have only a small effect on estimates of coarse PM. While it is 

quite possible that the very highest coarse PM concentration (indicated in Figure 

B-1(b) to be about 500 μg/m3) reflects the effect of exceptional winds, and might 

be excluded under the Exceptional Event rule, the next-highest values of PM10

light extinction almost certainly would also be dominated by coarse PM 

concentrations in the range of 150 to 200 μg/m3 and many might not be 

excludable. 

 Salt Lake City has extreme haze hours caused mostly by nitrate in the winter with 

some periods with carbonaceous PM2.5 being the major contributor. Because 

coarse PM in Salt Lake City was estimated by applying a regional factor to the 

local PM2.5 mass value, it would not have been possible for the results to indicate 

a significant coarse PM contribution to PM10 light extinction even if one existed 

at this site. However, from what EPA staff know of the area, it is unlikely that 

there is a frequent large contribution from coarse PM. The area typically has at 

most a few days per year with measured 24-hour average PM10 as high as 150-200 

μg/m3. If this were all coarse PM, the contribution to 24-hour average light 

extinction would be 90-120 Mm-1, with the possibility of much higher hourly 

contributions by coarse mass during these few days. 

 Dallas and Houston have high sulfate PM2.5 contributions to PM10 light extinction, 

but Dallas also has some winter hours with extreme PM10 light extinction with 

substantial contributions from nitrate and organic carbonaceous material, while 

Houston seems to have less contribution by nitrate. Because coarse PM in both 

Dallas and Houston was estimated by applying a regional factor to the local PM2.5 

mass value, it would not have been possible for the results to indicate a significant 

coarse PM contribution to PM10 light extinction even if one existed at this site. 

However, from what EPA staff know of the areas, it is unlikely that there is a 

frequent large contribution from coarse PM. Houston typically has at most a few 

days per year with measured 24-hour average PM10 as high as 150-200 μg/m3

. If 

this were all coarse PM, the contribution to 24-hour average light extinction 

would be 90-120 Mm-1. Dallas typically does not have PM10 as high as 150 

μg/m3. 

 Sulfate in the summer and nitrate in the fall and winter are responsible for most of 

the extreme PM10 light extinction at St. Louis, though there are several maximum 
3-84 

hourly periods where coarse PM is a major component. Recall that estimates of 

coarse PM in St. Louis may be affected by a very local source (see Appendix A), 

and thus the instances of high PM10 light extinction due to coarse PM may be 

limited in geographic scope.31

 Birmingham and Atlanta are similar in having sulfate year-round and winter 

carbonaceous PM2.5 as major contributors to their extreme PM10 light extinction 

periods. Coarse PM for Birmingham was estimated using data from a single site, 

and the estimates should be reasonably representative. Coarse PM for Atlanta 

was estimated using data from two fairly close sites and the estimates should be 

reasonably representative. 

 Detroit has frequent large light extinction contributions from nitrate PM2.5, mostly 

in the winter, as well as some contributions from sulfate PM2.5 year-round and 

several fall and winter days with high contributions from carbonaceous PM2.5. 

Coarse PM makes a notable contribution on a few days. Coarse PM for Detroit 

was estimated using data from a single site near an automobile plant, and the 

estimates should be reasonably representative for that site. 

 The remaining four urban locations (Pittsburgh, Baltimore, Philadelphia, and New 

York) are similar in that most of their extreme PM10 light extinction is from yearround combinations of sulfate and nitrate. New York also has some winter 

elemental and organic carbonaceous contributions to its extreme PM10 light 

extinction. Recall that the PM2.5 site representing the New York area is actually in 

Elizabeth, NJ; emissions from diesel trucks on nearby interstate highways and/or 

diesel engines associated with port activities might explain the carbonaceous 

contributions. Coarse PM for Baltimore and Philadelphia was estimated using 

data from a single site in each area, and the estimates should be reasonably 

representative. Coarse PM for New York was estimated using data from two 

fairly distant sites and the estimates may not be representative of both sites. 

Because coarse PM was estimated for Pittsburgh by applying a regional factor to 

the local PM2.5 mass value, it would not have been possible for the results to 

indicate a significant coarse PM contribution to PM10 light extinction even if one 

existed at this site. However, exceedances of the PM10 NAAQS are rare in 

 

31 Comments concerning unrealistically high PM10-2.5 values for St. Louis are viewed as credible, but were 

received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all 

portions of this document. However, the text has been revised to caution readers with respect to the St. Louis 

results, and they will not be included in the visibility effects discussion in the final PM Policy Assessment 

document. Some graphics have been updated to exclude St. Louis results 
3-85 

Pittsburgh suggesting that coarse PM likely is not a frequent significant 

contributor to PM10 light extinction. 

3.5 POLICY RELEVANT BACKGROUND 

Policy relevant background levels of PM10 light extinction have been estimated for this 

assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emissions in 

the U.S., Canada, and Mexico were omitted, as described in the ISA. Estimates of PRB for PM10

light extinction were calculated from modeled concentrations of PM2.5 components using the 

IMPROVE algorithm. The necessary component concentrations were extracted from the CMAQ 

output files, as they were not summarized in the final ISA. More detail is provided in Appendix 

C. 

It is also necessary to have estimates of PRB for PM10-2.5, as input to the IMPROVE 

algorithm. The final ISA for this review does not present any new information on this subject. 

The approach used in the two previous reviews was to present the historical range of annual 

means of PM10-2.5 concentrations from IMPROVE monitoring sites selected as being least 

influenced by anthropogenic emissions (US EPA, 2004, Table 3E-1). For this assessment, EPA 

staff estimated PRB for PM10-2.5 using a contour map based on average 2000-2004 PM10-2.5

concentrations from all IMPROVE monitoring sites, found in a recent report from the 

IMPROVE program (DeBell, 2006). More detail is provided in Appendix C. 

The outcome of the procedures for estimating PRB consists of hour-specific estimates of 

PRB for PM2.5 components and annual average estimates for PRB for PM10-2.5. Thus, hourspecific estimates of PM10 light extinction are possible, using the same hour-specific relative 

humidity values as for the estimate of current conditions PM10 light extinction. 

In addition to allowing confirmation of the obvious fact that current conditions PM10 light 

extinction values are generally well above PRB conditions, the PRB estimates only play a role in 

this assessment in the estimation of “what if” scenarios representing compliance with alternative 

NAAQS scenarios based on PM10 light extinction. This role is described in section 4.1.4. 
This page left intentionally blank. 
4-1

4 PM10 LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS 

OF JUST MEETING SPECIFIC ALTERNATIVE SECONDARY 

NAAQS 

4.1 ALTERNATIVE SECONDARY NAAQS BASED ON PM10 LIGHT 

EXTINCTION AS THE INDICATOR 

4.1.1 Indicator and Monitoring Method 

The indicator considered for the UFVA is PM10 light extinction, assumed to be measured 

by a continuous instrument, or instrument pair, capable of reporting both light scattering and 

light absorption. EPA staff prepared a White Paper on Particulate Matter (PM) Light Extinction 

Measurements (US EPA, 2010d) for the purpose of soliciting comments on prospective 

measurement methods from the public and the Ambient Air Monitoring and Methods 

Subcommittee (AAMMS). In its review (Russell and Samet, 2010), the AAMMS made the 

recommendation to EPA that direct measurements be limited to PM2.5 light extinction as this can 

be accomplished by a number of commercially available instruments and because PM2.5 is 

generally responsible for most of the PM visibility impairment in urban areas. They indicated 

that it is technically more challenging at this time to accurately measure the PM10-2.5 component 

of light extinction. These recommendations were received subsequent to completion of the 

assessments described here, so they did not influence the use of PM10 light extinction as the 

indicator. 

4.1.2 Alternative Secondary NAAQS Scenarios Based on PM10 Light Extinction 

Eighteen alternative NAAQS scenarios presented in Table 4-1 are analyzed in this 

section. Nine are based on daily maximum daylight 1-hour PM10 light extinction and nine on 

PM10 light extinction in all hours without the restriction to daily maxima. Within each set of 

nine, the scenarios are ordered from least to most stringent. 
4-2

Table 4-1. Alternative Secondary NAAQS Scenarios for PM10 Light Extinction 

Level Annual 

Percentile Form 

Scenarios Based on Daily Maximum Daylight 1-Hour PM10 Light Extinction 

(a) 191 Mm-1 90 3-year average of percentile value 

(b) 191 Mm-1 95 3-year average of percentile value 

(c) 191 Mm-1 98 3-year average of percentile value 

(d) 112 Mm-1 90 3-year average of percentile value 

(e) 112 Mm-1 95 3-year average of percentile value 

(f) 112 Mm-1 98 3-year average of percentile value 

(g) 64 Mm-1 90 3-year average of percentile value 

(h) 64 Mm-1 95 3-year average of percentile value 

(i) 64 Mm-1 98 3-year average of percentile value 

Scenarios Based on Daylight 1-Hour PM10 Light Extinction (All Daylight Hours) 

(j) 191 Mm-1 90 3-year average of percentile value 

(k) 191 Mm-1 95 3-year average of percentile value 

(l) 191 Mm-1 98 3-year average of percentile value 

(m) 112 Mm-1 90 3-year average of percentile value 

(n) 112 Mm-1 95 3-year average of percentile value 

(o) 112 Mm-1 98 3-year average of percentile value 

(p) 64 Mm-1 90 3-year average of percentile value 

(q) 64 Mm-1 95 3-year average of percentile value 

(r) 64 Mm-1 98 3-year average of percentile value 

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on 

Measured PM10 Light Extinction 

It is useful to think ahead tentatively to monitor siting aspects of NAAQS 

implementation, so that the results presented in the remainder of this chapter based on the 15 

specific study sites can be better interpreted in terms of how well they might represent later 

findings if these (and other) areas were to deploy PM10 light extinction measurement instruments 

as part of implementing a secondary NAAQS. 

In light of the recommendations of the AAMMS (Russell and Samet, 2010), it is most 

likely that the instruments that would be used if directly measured PM2.5 light extinction were 

selected as the indicator to implement a secondary NAAQS would be “closed path” instruments 

that react only to air quality in their immediate vicinity. However, light paths that matter to 

perceived visual air quality are likely to be several kilometers long. Therefore, a monitoring site 

should be at least neighborhood in scale, i.e., its relationship to emission sources and transport 

should be such that measurements made at the site reasonably reflect concentrations in an area 

surrounding the site of at least about 0.5 to 4 kilometers in diameter. The AAMMS 
4-3

recommendations also include advice concerning network design, and probe and siting criteria 

applicable to a program of directly measuring PM2.5 light extinction.1

With regard to the monitoring sites used in this assessment, all are reported to be, or 

appear to be, neighborhood or larger scale, and all are in areas where people are present during 

daylight hours. The sites in Detroit (Dearborn) and New York (Elizabeth, NJ) are, however, 

rather close to an industrial source and a major interstate highway interchange/turnpike exit, 

respectively. Significantly, most of the study sites are not the highest PM2.5 concentration site in 

their urban area, so a “what if” scenario that manipulates the “current conditions” at these sites to 

“just meet” an alternative secondary NAAQS might implicitly leave other parts of their urban 

areas with PM2.5 light extinction above the NAAQS. 

4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary 

NAAQS Based on Measured PM10 Light Extinction 

Before modeling “what if” conditions, EPA staff augmented the data set described in 

Table 4 so that the sets of study days for Houston and Phoenix were seasonally balanced despite 

the lack of actual monitoring data for one quarter in each city. For the first quarter of 2005 in 

Phoenix, we substituted the available 12 days from the first quarter of 2006. For the fourth 

quarter of 2007 in Houston, we substituted 13 randomly drawn days from the fourth quarters of 

2005 and 2006. 

Also, Tacoma (originally) and Phoenix (after this augmentation) each have only two 

calendar years of suitable data, while the form of the alternative NAAQS scenarios requires the 

averaging of the 90th, 95th, or 98th percentile values from three years. In Tacoma and Phoenix, 

for every step in the analysis at which a design value is used as an input or reported as an output, 

we averaged the percentile values from the only two available years. 

We modeled all daylight and daily maximum daylight 1-hour PM10 light extinction under 

each of the “what if” scenarios (in which each study area “just meets” one of the 18 alternative 

secondary NAAQS listed in section 4.1.2) via the following steps. These steps are essentially the 

same as the “proportional rollback” steps that have been used in the health risk assessment 

modeling of “what if” conditions in several previous NAAQS reviews for PM and other criteria 

pollutants. The steps are described here for the nine scenarios based on daily maximum daylight 

1-hour PM10 light extinction; similar steps were followed for the nine scenarios based on 

percentiles of all daylight 1-hour PM10 light-extinction. The referenced tables present results for 

both sets of scenarios. 

 

1

 In chapter 4 of the second review draft of the PM Policy Assessment (US EPA, 2010c), EPA staff 

considers as an alternative to directly measuring PM2.5 light extinction the use of speciated PM2.5 mass-calculated 

light extinction by a method similar in concept to but simpler than the method described in section 3.2. Much of the 

monitoring infrastructure needed to implement this approach is already deployed by state and local air agencies. 
4-4

1. After excluding hours with relative humidity greater than 90 percent, identify the 

appropriate percentile (90th, 95th, or 98th) daily maximum daylight 1-hour PM10 light 

extinction value in each year, noting the day and hour each occurred, and average 

these values across years to calculate the PM10 light extinction design value for each 

site consistent with the percentile form of the NAAQS scenario.2

 The three resulting 

design values for each area (for the 90th, 95th, and 98th percentile forms) are shown in 

Table 4-2. (Note that in a number of cases, which are identified by a footnote, the 

study area meets one or more of the NAAQS scenarios under current conditions. In 

these cases, the “current conditions” PM10 light extinction values are not adjusted, 

i.e., PM10 light extinction values are never “rolled up.”) Notice that the design values 

for the 90th percentile maximum daily 1-hour for most cities are generally similar to 

the design values for the 98th percentile of all daylight hours. On average there are 

about ten hours defined as daylight per day, so if the PM10 light extinction were 

randomly distributed among the daylight hours and days, the 90th percentile 

maximum daily 1-hour would correspond to the 99th percentile of all hours; the fact 

that the point of rough equivalency is the 98th percentile indicates a tendency for 

hours with higher PM10 light extinction to cluster together in the same day. Figure 4-

1 presents two scatter plots that relate the design values based on daily maximum 1-

hour PM10 light extinction values and the design values based on all daylight 1-hour 

PM10 light extinction values. In Panel A, design values for the daily maximum and all 

hours forms are paired by the defining percentile, and colors are used to distinguish 

the 90th, 95th, and 98th percentile statistical forms. It appears from Panel A that the 

design values for the two approaches to defining the NAAQS scenarios are highly 

correlated but with the all hours approach resulting in numerically lower design 

values than the daily maximum approach. The correlation breaks down for the 98th

percentile form for the few study areas with the highest levels of PM10 light 

extinction. Panel B compares the 90th percentile design values based on daily 

maximum PM10 light extinction with the 90th, 95th, and 98th percentile design values 

based on all daylight hours PM10 light extinction. There is close agreement between 

the 90th percentile design values based on daily maximum values and the 98th

percentile design value based on all daylight hours. 

 

2

 Annual percentile values were picked from the set of day-specific or hour-specific estimates according to 

the same scheme as used for the current 24-hour secondary PM2.5 standard, as explained in section 4.5(a) of 40 CFR 

50 Appendix N. For example, if there are 60 daily maximum values in a year, the second highest value is the 98th

percentile value. Note that this differs from the algorithm used by some spreadsheet and other statistical programs, 

which may interpolate between sample values. Also, this is a different approach than that used in the Regional Haze 

program, in which conditions in the best and worst 20 percent of days are averaged together, rather than focusing on 

conditions on the specific day at the 80th and 20th percentile points. 
4-5

Table 4-2. Current Conditions PM10 Light Extinction Design Values for the Study 

Areas. 

Study Area 

Design Value for 

90th Percentile 

Form (Mm-1) 

Design Value for 

95th Percentile 

Form (Mm-1) 

Design Value for 

98th Percentile 

Form (Mm-1) 

Design Values Based on Daily Maximum Daylight 1-Hour PM10 Light Extinction

Tacoma 140* 157* 211 

Fresno 330 460 530 

Los Angeles 454 550 611 

Phoenix 105* 144* 185 

Salt Lake City 163* 252 409 

Dallas 184* 239 301 

Houston 194 234 291 

St. Louis 306 380 467 

Birmingham 357 483 562 

Atlanta 249 288 331 

Detroit 308 471 644 

Pittsburgh 278 313 364 

Baltimore 246 286 326 

Philadelphia 285 334 374 

New York 306 354 451 

Design Values Based on Daylight 1-Hour PM10 Light Extinction (All Daylight Hours)

Tacoma 76* 105* 136* 

Fresno 188* 261 368 

Los Angeles 261 341 441 

Phoenix 68* 79* 94* 

Salt Lake City 93* 141* 225 

Dallas 113* 143* 188* 

Houston 105* 128* 171* 

St. Louis 193 235 290 

Birmingham 173* 227 309 

Atlanta 166* 195 238 

Detroit 212 251 315 

Pittsburgh 167* 209 264 

Baltimore 171* 225 262 

Philadelphia 183* 222 278 

New York 186* 243 299 

* This design value meets one or more of the NAAQS scenarios based on PM10 light extinction. 
4-6

Figure 4-1. Comparison of Daily Max and All Daylight Hour Design Values for PM10

Light Extinction 

(A) Comparison of Design Values Matched by Percentile Form 

(B) Comparison of 90th Percentile Daily Maximum Design Values and 90th, 95th, and 98th

Percentile All Daylight Hours Design Values 
4-7

2. Using the same days and hours, find the three (or two, in the case of Phoenix and 

Houston for which there were only two years of suitable data available) 

corresponding values of PRB PM10 light extinction, and average these values across 

years to calculate the PRB portion of the design value. 

3. Subtract the value from step 2 from the value from step 1, to determine the non-PRB 

portion of the design value. 

4. Calculate the percentage reduction required in non-PRB PM10 light extinction in 

order to reduce the design value to the PM10 light extinction level that defines the 

NAAQS scenario, using the following equation: 

Percent reduction required = 1 – (NAAQS level – PRB portion of the design value)/ 

(non-PRB portion of the design value) 

The percentage reductions determined in step 4 are shown in Table 4-3. Figure 4-2 

presents them graphically in the form of a scatter plot, comparing the required 

reductions for scenarios based on daily maximum 1-hour daylight PM10 light 

extinction values to scenarios with the same level and percentile form but based on all 

daylight hours 1-hour PM10 light extinction values. For the NAAQS scenarios 

involving higher levels and lower percentile forms, there are some notable differences 

in the percentage reductions required for some area to attain. As was the case for the 

design values, notice in Table 4-3 that there are generally similar percentage 

reductions for each city and level for the 90th percentile maximum daily and 98th 

percentile of all daylight hours. 

As already stated, if the study area is meeting a NAAQS scenario in the current 

conditions case, no adjustments were made to represent the “just meeting” case. In 

effect, negative values for the percent reduction required to meet the NAAQS 

scenario calculated by the above equation were re-set to zero. 

5. Turning to the entire set of day/hour-specific actual and PRB daylight PM10 light 

extinction values for the three (or two) year period, determine the non-PRB portion of 

PM10 light extinction in that hour, reduce it by the percentage determined in step 4, 

and add back in the PRB PM10 light extinction. The result is the “just meets” PM10

light extinction value for that day and hour. 

Note that in these steps, it is not necessary to make any explicit or implicit assumption 

about what PM components would be reduced to allow the area to meet the NAAQS scenario, as 

the NAAQS scenario’s target design value is itself in units of light extinction. One path to 

meeting a NAAQS scenario would be to reduce each of the five PM2.5 components (and thus the 

annual and 24-hour design values shown in Table 3-2) and PM10-2.5 by the calculated “percent 

reduction required”. However, a lesser reduction in one or more of the six PM concentrations 

could be offset by a greater reduction in one or more of the remaining concentrations. Thus, it is 
4-8

not possible to associate unique values of annual average and 24-hour average PM2.5 with the 

“just meeting” NAAQS scenarios reported in Table 4-3. 
4-9

Table 4-3. Percentage Reductions in Non-PRB PM10 Light Extinction Required to “Just 

Meet” the NAAQS Scenarios Based on Measured Light Extinction (Mm-1)

3

NAAQS Scenarios Based on Daily Maximum 1-Hour Daylight PM10 Light Extinction 

Scenario (a) (b) (c) (d) (e) (f) (g) (h) (i) 

Level/Form 191/90th 191/95th 191/98th 112/ 90th 112/95th 112/98th 64/90th 64/95th 64/98th

Area Percentage Reduction Required in Non-PRB PM10 Light Extinction

Tacoma 0 0 10 22 31 52 59 63 78 

Fresno 43 59 64 67 76 79 82 87 89 

Los Angeles 59 66 69 76 81 82 87 89 90 

Phoenix 0 0 0 0 22 40 39 56 66 

Salt Lake City 0 24 54 32 56 73 61 75 85 

Dallas 0 21 38 41 54 65 69 75 81 

Houston 2 20 35 44 56 63 70 78 80 

St. Louis 38 51 60 64 72 77 80 85 88 

Birmingham 48 61 67 70 77 81 84 87 90 

Atlanta 24 35 44 57 63 68 77 80 83 

Detroit 39 60 71 65 77 83 80 87 91 

Pittsburgh 32 40 48 60 65 70 78 81 83 

Baltimore 23 34 42 56 63 67 76 80 82 

Philadelphia 34 43 49 62 67 71 79 82 84 

New York 38 47 58 64 69 76 80 83 87 

NAAQS Scenarios Based on 1-Hour Daylight PM10 Light Extinction 

Scenario (j) (k) (l) (m) (n) (o) (p) (q) (r) 

Level/Form 191/90th 191/95th 191/98th 112/90th 112/95th 112/98th 64/90th 64/95th 64/98th

Area Percentage Reduction Required in Non-PRB PM10 Light Extinction

Tacoma 0 0 0 0 0 21 18 44 64

Fresno 0 27 49 41 58 70 67 77 84

Los Angeles 27 45 57 58 68 75 77 82 86

Phoenix 0 0 0 0 0 0 5 19 32

Salt Lake City 0 0 15 0 21 51 32 55 72

Dallas 0 0 0 1 23 42 44 58 68

Houston 0 0 0 0 13 37 40 51 67

St. Louis 1 19 35 43 53 62 68 74 79

Birmingham 0 16 39 36 52 65 65 74 81

Atlanta 0 2 20 33 44 55 63 70 75

Detroit 10 24 40 48 56 65 71 75 81

Pittsburgh 0 9 28 34 47 58 63 70 77

Baltimore 0 15 28 35 51 58 64 73 77

Philadelphia 0 14 32 39 51 61 66 73 78

New York 0 22 36 40 55 63 67 75 79

 

3

 As a result of a formula error, the intended values of PRB PM10 light extinction were not properly used in 

calculating the entries in this table, generally resulting in the required reductions shown here to be slightly smaller 

than they should be. For a more detailed explanation of this issue, see the 2010 Lorang memo, "Explanation of 

Error in Table 4-3 (Percentage reductions in non-PRB PM10 light extinction required to 'just meet' the NAAQS 

scenarios based on measured light extinction) of the final Urban Focused Visibility Assessment", July 23, 2010. As 

discussed in that memo, the only other results presented in this document that were affected by this error were 

Figure 4-2, Figure 4-3(a), and Panels (a) through (r) of Appendix F. The effect on those figures was judged too 

negligible to warrant regenerating them for this final version. 
4-10

Figure 4-2. Comparison of Required Percentage Reductions in Non-PRB PM10 Light 

Extinction Needed to Meet NAAQS Scenarios

0

20

40

60

80

100

0 20 40 60 80 100

Based on Daily Maximum Daylight LE

Based on Daylight LE (all hours)

191/90th 191/95th 191/98th 112/90th 112/95th 112/98th 64/90th

64/95th 64/98th

4.2 ALTERNATIVE SECONDARY PM2.5 NAAQS BASED ON ANNUAL AND 

24-HOUR PM2.5 MASS 

4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-Hour PM2.5 Mass 

In this final assessment, EPA staff have modeled two “what if” scenarios using the same 

indicators and averaging periods as defined in the current suite of PM2.5 NAAQS set in 2006. 

The first scenario uses the current suite of PM2.5 NAAQS levels and the second a suite of lower 

levels considered in the health risk assessment (US EPA, 2010e): 

 15 μg/m3

 weighted annual average PM2.5 concentration and 35 μg/m3 24-hour average 

PM2.5 concentration with a 98th percentile form, both averaged over three years. 

 12 μg/m3

 weighted annual average PM2.5 concentration and 25 μg/m3 24-hour average 

PM2.5 concentration with a 98th percentile form, both averaged over three years. 
4-11

4.2.2 Approach to Modeling Conditions If Secondary PM2.5 NAAQS Based on 

Annual and 24-Hour PM2.5 Mass Were Just Met 

Because these NAAQS scenarios are based on PM2.5 mass as the indicator, rather than 

light extinction, the steps needed to model “what if” conditions are somewhat different, and 

involve explicit consideration of changes in PM2.5 components. 

1. Apply proportional rollback to all the PM2.5 monitoring sites in each study area, 

taking into account PRB PM2.5 mass, to “just meet” the NAAQS scenario for the area 

as a whole, not just at the visibility assessment study site. The health risk assessment 

document describes this procedure in detail. The degree of rollback is controlled by 

the highest annual or 24-hour design value, which in most study areas is from a site 

other than the site used in this visibility assessment. The relevant result from this 

analysis is the percentage reduction in non-PRB PM2.5 mass need to “just meet” the 

NAAQS scenario, for each study area. These percentage reductions are shown in 

Table 4-4. Note that Phoenix and Dallas meet the 15/35 NAAQS scenario under 

current conditions, and require no reduction. PM2.5 levels in these two cities were not 

“rolled up.” 

2. For each day and hour for each PM2.5 component, subtract the PRB concentration 

from the current conditions concentration, to determine the non-PRB portion of the 

current conditions concentration. 

3. Apply the percentage reduction from step 1 to the non-PRB portion of each of the 

five PM2.5 components. Add back the PRB portion of the component. 

4. Re-apply the IMPROVE algorithm (section 3.2.3), using the reduced PM2.5 

component concentrations, the current conditions PM10-2.5 concentration for the day 

and hour, and relative humidity for the day and hour. Include the term for Rayleigh 

scattering. 
4-12

Table 4-4. Percentage Reductions Required in Non-PRB PM2.5 Mass to “Just Meet” 

NAAQS Scenarios Based on Annual and 24-Hour PM2.5 Mass 

Study Area 

Percentage Reduction Required 

(s) 

Annual PM2.5 NAAQS = 15 μg/m3

24-Hour PM2.5 NAAQS = 35 μg/m3

(t) 

Annual PM2.5 NAAQS = 12 μg/m3

24-Hour PM2.5 NAAQS = 25 μg/m3

Tacoma 19 43 

Fresno 45 61 

Los Angeles 37 55 

Phoenix 0* 22 

Salt Lake City 37 56 

Dallas 0* 7 

Houston 6 27 

St. Louis 10 37 

Birmingham 22 45 

Atlanta 8 30 

Detroit 19 43 

Pittsburgh 19 43 

Baltimore 6 33 

Philadelphia 8 35 

New York 17 41 

* These areas meet this NAAQS scenario under current conditions.

4.3 RESULTS FOR EACH “JUST MEET” ALTERNATIVE SECONDARY 

NAAQS SCENARIO 

The modeling described in sections 4.1 and 4.2 resulted in estimates of PM10 light 

extinction for each day and hour in each study area, for each NAAQS scenario. Four summaries 

of these conditions are presented here. Figure 4-3 shows two box-and-whisker plots of daily 

maximum daylight 1-hour PM10 light extinction. The top panel (a) is for the single illustrative 

scenario of a NAAQS based on daily maximum daylight 1-hour PM10 light extinction with a 

level of 112 Mm-1 and a 90th percentile form, which was chosen for this illustration because it is 

approximately mid-way among the nine scenarios based on daily maximum PM10 light extinction 

in terms of stringency.4 The bottom panel (b) is for the scenario of meeting the current suite of 

secondary PM2.5 NAAQS standards: 15 μg/m3 annual average and 35 μg/m3 24-hour average 

(98th percentile form). A notable feature of this comparison is that in the top panel, all the study 

 

4

 Plots of the distribution of daily maximum PM10 light extinction for all 18 NAAQS scenarios based on 

daily maximum PM10 light extinction, and of individual hourly PM10 light extinction for all 18 NAAQS scenarios 

based on individual daylight hours, are provided in Appendix F. 
4-13

areas have a similar distribution of the daily maximum daylight 1-hour PM10 light extinction, 

while in the bottom panel this is not the case. This is expected, since a NAAQS based on a 

measured daily maximum PM10 light extinction indicator will of course result in areas achieving 

similar daily maximum PM10 light extinction patterns once each area reaches a “just meets” 

condition. In areas with generally higher relative humidity conditions, concentrations of PM2.5 

components and/or PM10-2.5 would need to be lower to achieve the “just meet” condition. In 

contrast, in the NAAQS scenario represented by the bottom panel, concentrations of PM2.5 mass 

will be similar across areas, but concentrations of PM2.5 components may not be, and levels of 

PM10 light extinction will not be similar in areas with dissimilar levels of relative humidity. The 

specific differences among areas in the bottom panel are generally as expected, with the drier 

study areas having lower levels of PM10 light extinction. 

Tables 4-5 and 4-6 summarize the “just meet” conditions in the NAAQS scenarios in 

terms of the PM10 light extinction design values. Table 4-5 addresses the 18 scenarios of 

NAAQS based on measured PM10 light extinction. When an area just meets a NAAQS scenario, 

its design value in principle should exactly equal the NAAQS level, so preparation of this table 

serves as a check against calculation errors. Note that the design values in Table 4-5, resulting 

from the rollback steps described in section 4.1.4, in some cases do not exactly equal the 

assumed level of the NAAQS, although all are quite close. Closer investigation has revealed that 

this is mostly a result of hours switching their ranking in the rollback process. Hours can switch 

rank because the level of PRB PM10 light extinction varies with each hour, so a uniform 

percentage reduction in non-PRB light extinction (step 5) can result in non-uniform percentage 

reductions in actual PM10 light extinction; a lower ranking hour can thereby move up in the postrollback ranking. In principle, rollback could be iterated to exactly achieve a design value equal 

to the level of the NAAQS for each scenario. However, the discrepancies indicated in Table 4-5 

were judged too small to justify iterative rollback, given other uncertainties in the analysis. 

Table 4-6 addresses the two scenarios of NAAQS based on PM2.5 mass, with PM10 light 

extinction design values shown for the 90th, 95th , and 98th percentile forms. 

 
4-14

Figure 4-3. Distributions of Daily Maximum Daylight 1-Hour PM10 Light Extinction Under Two  Secondary NAAQS Scenarios (Excluding Hours with Relative Humidity Greater Tha

(a) Secondary NAAQS Based on Daily Maximum Daylight 1-Hour PM10 Light Extinction with a Level of 

Percentile Form 
4-15

Figure 4-3. Distributions of Daily Maximum Daylight 1-Hour PM10 Light Extinction Under Two “Just 

NAAQS Scenarios (Excluding Hours with Relative Humidity Greater Than 90 Percent), con

(b) Secondary NAAQS of 15 μg/m3 for the Annual Average and 35 μg/m3 for the 98th Percentile 24-
4-16

Table 4-5. PM10 Light Extinction Design Values for “Just Meet” Secondary NAAQS 

Scenarios Based on Measured PM10 Light Extinction (Excluding Hours with 

Relative Humidity Greater Than 90 Percent) 

Secondary NAAQS Scenarios Based on Daily Maximum

(a) (b) (c) (d) (e) (f) (g) (h) (i) 

Level (Mm-1) 191 191 191 112 112 112 64 64 64 

Percentile Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

PM10 Light Extinction Design Value 

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 140 157 191 112 115 108 66 74 58 

Fresno, CA 191 191 191 113 112 112 65 64 64 

Los Angeles, CA 191 191 191 113 112 112 65 64 64 

Phoenix, AZ 105 144 185 105 112 112 64 64 64 

Salt Lake City, UT 163 191 191 112 112 112 65 64 64 

Dallas, TX 184 191 191 112 113 112 64 65 65 

Houston, TX 191 191 191 114 111 112 67 61 67 

St. Louis, IL 191 191 191 112 112 112 65 64 64 

Birmingham, AL 191 191 191 113 113 112 64 66 64 

Atlanta, GA 191 191 191 112 112 112 64 64 65 

Detroit, MI 191 191 191 112 112 112 64 64 65 

Pittsburgh, PA 191 191 191 112 112 112 64 64 64 

Baltimore, MD 191 191 191 111 112 112 63 64 64 

Philadelphia, PA 191 191 191 112 112 112 64 64 64 

New York, NY 191 191 191 112 112 112 64 64 64 

Secondary NAAQS Scenarios Based on All Daylight Hours

(j) (k) (l) (m) (n) (o) (p) (q) (r) 

Level (Mm-1) 191 191 191 112 112 112 64 64 64 

Percentile Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

PM10 Light Extinction Design Value 

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 76 105 136 76 105 112 63 64 59

Fresno, CA 188 192 191 112 113 111 65 64 63

Los Angeles, CA 191 191 191 112 113 112 64 65 64

Phoenix, AZ 68 79 94 68 79 94 64 64 64

Salt Lake City, UT 93 141 191 93 112 112 64 64 64

Dallas, TX 113 143 188 112 112 113 65 64 66

Houston, TX 105 128 171 105 113 111 65 66 61

St. Louis, IL 191 191 191 112 112 112 65 64 65

Birmingham, AL 173 191 191 112 112 112 65 64 65

Atlanta, GA 166 191 192 113 112 113 65 63 65

Detroit, MI 191 191 191 112 112 112 64 65 64

Pittsburgh, PA 167 191 191 113 113 112 65 65 65

Baltimore, MD 171 191 191 112 112 112 64 65 64

Philadelphia, PA 183 191 191 112 112 113 65 64 65

New York, NY 186 191 192 112 112 113 65 65 65
4-17

Table 4-6. PM10 Light Extinction Design Values for “Just Meet” Secondary NAAQS 

Scenarios Based on PM2.5 Mass (Excluding Hours with Relative Humidity 

Greater Than 90 Percent) 

Annual/24-Hour 

PM2.5 NAAQS 

(s) 

15μg/m3 / 35μg/m3

(t) 

12μg/m3 / 25μg/m3

City Name 

90th %tile 

Design Value 

(Mm-1) 

95th %tile 

Design Value 

(Mm-1) 

98th %tile 

Design Value 

(Mm-1) 

90th %tile 

Design Value 

(Mm-1) 

95th %tile 

Design Value 

(Mm-1) 

98th %tile 

Design Value 

(Mm-1) 

Design Values Based on Daily Maximum Daylight 1-Hour PM10 Light Extinction

Tacoma, WA 119 131 178 93 102 136 

Fresno, CA 191 266 304 142 196 224 

Los Angeles, CA 312 361 429 231 261 355 

Phoenix, AZ 105* 143* 185* 96 135 161 

Salt Lake City, UT 110 167 269 83 125 198 

Dallas, TX 183* 239* 301* 172 224 282 

Houston, TX 185 222 276 148 178 220 

St. Louis, IL 286 354 441 252 289 363 

Birmingham, AL 285 394 464 213 300 365 

Atlanta, GA 230 266 307 181 208 243 

Detroit, MI 256 387 536 187 277 401 

Pittsburgh, PA 229 258 299 167 188 218 

Baltimore, MD 233 272 308 169 202 221 

Philadelphia, PA 263 308 346 190 222 254 

New York, NY 255 295 376 182 213 268 

Design Values Based on Daylight 1-Hour PM10 Light Extinction (All Daylight Hours)

Tacoma, WA 65 88 113 52 70 84 

Fresno, CA 110 152 214 83 113 160 

Los Angeles, CA 173 228 294 129 169 220 

Phoenix, AZ 68* 79* 94* 60 70 86 

Salt Lake City, UT 63 95 150 49 72 112 

Dallas, TX 113* 143* 188* 106 134 176 

Houston, TX 99 122 163 81 99 131 

St. Louis, IL 180 221 271 147 183 237 

Birmingham, AL 140 183 247 105 138 186 

Atlanta, GA 154 180 220 123 144 174 

Detroit, MI 175 208 257 130 155 188 

Pittsburgh, PA 138 173 218 102 127 159 

Baltimore, MD 163 213 248 121 155 184 

Philadelphia, PA 169 205 258 123 149 187 

New York, NY 155 203 249 113 148 178 

* Phoenix and Dallas meet 15 μg/m3/35 μg/m3 under current conditions, so these entries are essentially the same as 

for current conditions. 
4-18

Table 4-7 summarizes all 20 scenarios in terms of the percentage of days (across 2005 to 

2007, but after rollback) in which the daily maximum daylight 1-hour PM10 light extinction 

under “just meeting” conditions exceeds each of the CPLs. Part A of the table applies to 

NAAQS scenarios based on daily maximum 1-hour PM10 light extinction values. Part B of the 

table applies to the scenarios based on 1-hour PM10 light extinction values during all daylight 

hours. Note that the reported percentages in both Part A and Part B is the percentage of days in 

which the daily maximum daylight 1-hour PM10 light extinction under “just meet” conditions 

exceeds each of the CPLs; this allows comparison of the “effectiveness” of the two NAAQS 

approaches using a consistent metric. (The 15/35 and 12/25 NAAQS scenarios are the same in 

Part A and Part B, and are repeated only for convenience in making comparisons.) Hours with 

relative humidity above 90 percent have been excluded from consideration, consistent with the 

definition of the NAAQS scenarios. Also shown at the bottom of the table in each column 

representing a NAAQS scenario is the average of these percentages of time across the 15 study 

areas (this is the simple column average, not weighted by the number of days available in each 

area). Comparisons of these percentages allows a rough indication of how the two scenarios of a 

NAAQS based on PM2.5 mass compare to the other 18 scenarios in terms of protecting visual air 

quality. Notice that the most restrictive of the two NAAQS scenarios based on PM2.5 mass 

would reduce the projected 1-hour maximum daily PM10 light extinction above the least 

restrictive CPL (191Mm-1) to less than 10 percent of the time for most of the urban areas (only 

L.A., St. Louis, and Birmingham have values above 10 percent).5

 However at the current PM 

NAAQS level (i.e., 15/35) all of the eastern urban areas and Los Angeles exceed the least 

restrictive CPL more than 10% of the time. Comparison of Parts A and B of Figure 4-7 indicates 

that basing a PM10 light extinction NAAQS scenario on daily maximum 1-hour PM10 light 

extinction has a lower percentage in excess of the 1-hour daily maximum versus the NAAQS 

scenario based on all daylight hours PM10 light extinction for a given level and percentile form of 

the NAAQS. This is consistent with the results presented in Table 4-2 and Figure 4-1, which 

indicated that current conditions design values are generally lower for the all hours approach. 

Again there is near equivalence between the 90th percentile daily maximum and 98th percentile 

all daylight hours in terms of the percent of days exceeding the daily maximum CPL values in 

Table 4-7.

 

5

 Comments were received concerning unrealistically high PM10-2.5 values for St. Louis and to a lesser 

extent for Los Angeles. High contributions by PM10-2.5 would help explain why a PM2.5 standard would be less 

effective in reducing visibility impacts. EPA staff view the comments concerning unreliable PM10-2.5 values for St. 

Louis as credible, but these comments were received too late in the review process to permit reanalysis using an 

alternate data set or to remove St. Louis from this document. However, the text has been revised to caution readers 

with respect to the St. Louis results, and they will not be forwarded to the visibility effects discussion in the PM 

Policy Assessment document. 
4-19

Table 4-7. Percentage of Days with Maximum 1-Hour Daylight PM10 Light Extinction Above 

Scenario Under “Just Meet” Conditions Across Three Years (or Two in the Case 

(A) NAAQS Scenarios Based on Daily Maximum 1-Hour PM10 Light Extinction 

Days with Max Hour Above 

64 Mm-1

Days with Max Hour Above 

112 Mm-1

Days 

Scenario a b c d e f g h i s t a b c d e f g h i s t a b c d NAAQS Level Mm-1

191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 1

NAAQS Percentile Form 

90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 9

Annual/24-Hour 

15/ 35 

12/ 25 

15/ 35 

12/ 25 

Area Percentage of days Percentage of days Percentage of 

Tacoma 52 52 48 40 34 16 12 10 2 43 28 22 22 14 11 7 2 1 1 0 11 5 4 4 3 1 Fresno 54 40 32 31 19 13 10 5 3 54 40 29 16 11 9 5 3 2 0 0 29 17 9 5 3 2 Los Angeles 74 64 58 43 32 27 12 6 3 85 79 40 31 26 11 6 3 1 0 0 69 52 11 6 3 1 Phoenix 44 44 44 44 27 10 10 5 2 44 40 6 6 6 6 5 2 2 1 1 6 6 1 1 1 1 Salt Lake City 44 27 13 24 11 5 10 5 1 24 15 17 11 5 10 5 1 4 1 0 9 6 8 5 1 4 Dallas 80 66 50 48 25 14 10 5 1 81 77 41 22 12 10 5 1 1 0 0 41 37 10 5 1 1 Houston 77 65 55 47 30 18 13 3 3 75 64 43 28 16 12 4 3 1 0 0 41 23 11 6 2 1 St. Louis 81 71 54 46 34 20 11 6 2 97 89 45 30 18 12 6 2 2 1 0 73 57 11 5 3 2 Birmingham 63 49 40 33 19 14 10 6 3 84 70 30 18 12 10 5 3 1 0 0 55 38 10 5 2 1 Atlanta 85 80 76 62 51 34 9 5 1 90 85 59 47 30 11 5 1 0 0 0 71 54 11 4 2 0 Detroit 74 52 41 46 22 6 11 4 3 80 74 45 17 6 11 4 3 4 1 0 61 49 10 4 3 4 Pittsburgh 70 63 54 40 31 23 9 6 2 79 63 37 29 21 9 6 1 0 0 0 48 28 9 5 1 0 Baltimore 67 61 54 41 30 25 11 4 2 78 64 39 29 23 11 5 2 1 0 0 47 31 12 6 2 1 Philadelphia 72 66 60 41 31 27 8 6 3 85 74 38 31 24 8 5 3 0 0 0 61 38 8 5 3 0 New York 63 59 40 33 27 18 9 6 2 76 62 32 24 16 9 6 2 1 0 0 46 30 10 6 2 1 Average 67 57 48 41 28 18 10 6 2 72 62 35 24 16 10 5 2 1 0 0 45 31 9 5 2 1 
4-20

 (B) NAAQS Scenarios Based on PM10 Light Extinction During All Daylight Hours* 

Days with Max Hour Above 

64 Mm-1

Days with Max Hour Above 

112 Mm-1

Days 

Scenario j k l m n o p q r s t j k l m n o p q r s t j k l 

NAAQS 

Level 

Mm-1 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 

NAAQS 

Percentil

e Form 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 

Annual/

24-

Hour 

15/ 

35 

12/ 

25 

15/ 

35 

12/ 

25 

Area Percentage of days Percentage of days 

Tacoma 52 52 52 52 52 40 40 23 10 43 28 22 22 22 22 22 11 12 4 1 11 5 4 4 4 

Fresno 76 65 51 56 41 27 30 17 7 54 40 52 37 25 30 17 7 9 4 1 29 17 29 17 7 

Los 

Angeles 86 83 76 75 61 46 40 27 14 85 79 73 58 42 41 27 14 10 3 1 69 52 42 27 13 

Phoenix 44 44 44 44 44 44 44 29 17 44 40 6 6 6 6 6 6 6 5 3 6 6 1 1 1 

Salt Lake 

City 44 44 33 44 27 15 24 12 6 24 15 17 17 14 17 12 5 9 5 2 9 6 8 8 5 

Dallas 80 80 80 80 63 45 42 21 11 81 77 41 41 41 41 21 10 9 4 1 41 37 10 10 10 

Houston 77 77 77 77 70 53 49 35 13 75 64 44 44 44 44 33 13 13 8 1 41 23 11 11 11 

St. Louis 98 92 84 78 65 51 39 27 15 97 89 76 62 48 39 27 15 9 4 2 73 57 39 27 14 

Birmingham 89 85 72 74 59 42 42 25 15 84 70 64 55 39 42 25 14 14 8 3 55 38 34 26 14 

Atlanta 91 91 87 81 76 63 51 31 14 90 85 75 73 62 48 30 12 5 1 0 71 54 31 30 12 

Detroit 84 78 72 66 57 45 41 27 10 80 74 63 55 45 41 26 9 6 4 4 61 49 40 26 9 

Pittsburgh 85 83 73 69 55 42 35 22 11 79 63 57 53 40 34 22 11 8 1 0 48 28 26 22 11 

Baltimore 80 72 66 60 44 38 28 14 10 78 64 50 43 34 28 14 9 4 2 1 47 31 23 14 9 

Philadelphia 86 82 73 68 59 43 34 23 9 85 74 63 58 41 33 24 8 6 1 0 61 38 31 24 8 

New York 83 74 64 62 44 34 31 18 11 76 62 59 42 32 30 18 10 8 3 1 46 30 28 18 10 

Average 77 73 67 66 54 42 38 23 12 72 62 51 44 36 33 22 10 9 4 1 45 31 24 18 9 

* Note that the table reports results based on daily maximum daylight hour, while the NAAQS scenarios in Pane

hours (in both cases excluding hours with RH>90%). 
5-1

5 SUMMARY 

This chapter integrates the key information on the purpose, approach, principal results, 

and significant technical issues of the assessment efforts that are characterized in greater detail in 

chapters 2, 3 and 4 and the appendices of this final Urban Focused Visibility Assessment 

(UFVA). Earlier versions of the UFVA1 document the original assessment and its evolution in 

response to Clean Air Science Advisory Committee (CASAC) and public comments. This 

chapter is organized by the three separate assessments that are described in greater detail in 

chapters 2 through 4. 

5.1 Urban Visibility Preference Studies Reanalysis 

Purpose: The overall purpose in conducting a reanalysis of urban preference studies is to 

determine whether there is a credible range of acceptable visual air quality conditions above 

which the national public welfare is adversely affected. Similar, though not identical, visibility 

preference studies were conducted in four metropolitan areas including Denver, CO; Vancouver, 

BC (Canada); Phoenix, AZ; and Washington, DC. These studies were performed separately to 

support the development of local visibility protection efforts, except for Washington, DC which 

was the subject of two separate pilot studies designed to better understand visibility preference 

studies. The common feature in each of these studies was that study participants were asked to 

individually rate the acceptability of scenic images shown to them one at a time in random order 

that depicted visibility impairment over a range of conditions from nearly pristine to highly 

impaired. 

Approach: The methodology used in the reanalysis involved a critical review of data 

generated for each study to identify issues of consistency and to identify whether and when it 

might be appropriate to compare/combine the studies’ results. The results were displayed as 

points on plots of percent of participants that rated each visibility condition acceptable versus the 

amount of visibility impairment as measured in deciview units (i.e., a logarithmic transformation 

of light extinction). Logit regression analysis was used to develop best fit curves for each of the 

four urban area study results and to determine whether they differed significantly from each 

other. 

Principal Results: Logit regression applied to the results for each of the four urban areas 

defined statistically significant relationships for each that are similarly shaped but with different 

visibility impairment threshold value, defined here as the 50th percentile acceptability criteria in 

 

1 Available at http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html
5-2

each urban area study. The range of 50th percentile values is from ~20 dv to ~30 dv, which when 

expressed in terms of PM10 light extinction corresponds to 64 Mm-1 to 191 Mm-1. The upper and 

lower bounds of the range and a mid-point value of 25 dv (corresponding to 112 Mm-1) were 

selected as Candidate Protective Levels (CPLs) to compare with the distributions of current 

conditions for 15 urban areas (chapter 3) and to define alternative standards to be included in the 

assessment (chapter 4). 

Technical Issues: Each of the four urban preference study areas produced well defined 

though statistically significant different results with respect to what visibility impairment level 

divides acceptable from unacceptable conditions. A number of hypotheses concerning why the 

results differed for each area are discussed in chapter 2. However, additional research to better 

understand why the response distributions differed by location could usefully inform future PM 

reviews. 

5.2 Current Visibility Conditions 

Purpose: The goal of this assessment is to develop a daylight hourly averaged PM10 light 

extinction dataset for several large urban areas to characterize current visibility conditions and 

compare them to the CPLs in order to determine the extent, frequency and causes of visibility 

impairment in cases where the CPLs are exceeded. 

Approach: A simple linear algorithm, the IMPROVE algorithm,2

 was used to estimate 

PM10 light extinction for 15 urban areas for the period from 2005 to 2007 from hourly PM2.5 and 

PM10-2.5 mass and PM2.5 component concentrations and relative humidity data (used to estimate 

the water component of the PM under ambient conditions). While PM2.5 mass concentration and 

relative humidity data are available from continuous instruments on an hourly averaged basis, 

PM2.5 composition data available from the Chemical Speciation Network (CSN) is based on 24-

hour average filter samples which are only collected on a one-day-in-three or one-day-in-six 

basis. The methodology used to estimate hourly daylight PM2.5 components involved use of 

CMAQ regional air quality modeling to generate monthly averaged species-specific diurnal 

patterns (ratio of each hour to the 24-hour mean) for each of the urban areas. These are used to 

calculate the hourly relative mix of species that are then scaled so that the sum of the PM2.5

component concentrations equals the measured hourly PM2.5 mass concentration. PM10

continuous monitoring data was available at a few of the urban areas permitting hourly PM10-2.5

concentrations to be determined by subtracting the hourly PM2.5 concentration. Elsewhere PM10-

2.5 was estimated using ratios of PM2.5 to PM10. The resulting estimates of hourly averaged PM2.5

component concentrations, PM10-2.5 mass concentration and the measured relative humidity were 

 

2

 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.) 
5-3

used as input to the IMPROVE algorithm to estimate hourly PM10 light extinction to be 

calculated for daylight hours with relative humidity no greater than 90%. Resulting daylight 1-

hour averaged PM10 light extinction values were compiled on the basis of all hours and 

maximum daily values for relative humidity conditions less than or equal to 90% to examine the 

frequency that they exceed CPLs by urban area. Tables and plots of the total and component 

contributions to PM10 light extinction were produced to characterize the nature and causes of 

visibility impairment. 

Principal Results: All of the results are for 1-hour PM10 light extinction during daylight 

hours when relative humidity is no greater than 90% for the 15 selected urban areas. 

 The use of this relative humidity cap significantly reduced the occurrence of visibility 

impairment caused by meteorological conditions like fog and precipitation. 

 Maximum daily hourly PM light extinction values exceeded the low, middle and high 

CPL 77%, 52% and 26% of the days, respectively, when averaged across the 15 urban 

areas. Eastern and California urban areas have the highest frequencies and nonCalifornia Western urban areas have the lowest frequencies above each CPL. 

 All hours PM10 light extinction values exceeded the low, middle and high CPLs 45%, 

22% and 7% of the hours, respectively, averaged across the 15 urban areas, with the 

Eastern and California urban areas having the highest frequencies and non-California 

Western urban areas having the lowest frequencies above each CPL. 

 The range of PM10 light extinction values and relative contributions by PM2.5 components 

for the most impaired 10% of maximum daily 1-hour hours are similar to the most 

impaired 2% of all hours, because they include hours from a large number of days in 

common. 

 During the most visibility impaired hours PM2.5 nitrate is the dominant light extinction 

contributor for several western urban areas (Fresno, LA, and Salt Lake City) while sulfate 

tends to be the largest contributor in the Eastern urban areas. Carbonaceous PM2.5 (i.e., 

organic mass plus elemental carbon) is a major contributor at Tacoma and a significant 

contributor at several other urban areas. Phoenix has significant light extinction 

contribution by PM10-2.5. Thus, regional differences in the dominant component 

contributing to visibility impairment are apparent. 

Technical Issues: The approach used to determine the hourly PM10-2.5 concentrations 

varied among the sites. Four of the sites had collocated continuous PM2.5 and PM10

measurements, while six urban areas used data from separate sites to determine the PM10-2.5 by 

difference, and the remaining five urban areas used regionally determined ratios of PM10-2.5 to 

PM2.5 to infer the PM10-2.5. The quality of the PM10-2.5 data inferred from separate sites for St. 

Louis have been called into question in review comments on an earlier version of the UFVA and 

are no longer viewed as credible. Therefore, the St. Louis results were appropriately labeled in 
5-4

the subsequent analyses in the final UFVA. A similar concern was raised regarding the LA data, 

but the PM10-2.5 values are low during most of the visibility impaired days so they are less of an 

issue compared with that of St. Louis and thus, were retained in these and subsequent 

assessments. PM10-2.5 is a small component of the light extinction for all of the other urban areas 

except for Phoenix, where high values are considered more plausible. Collocated continuous 

PM10 and PM2.5 monitoring at a greater number of urban areas would better address this issue in 

the future. 

While yielding generally reasonable results, the process employed to develop the hourly 

PM10 component information used as input to the IMPROVE algorithm is complex and subject 

to comments that there are alternate approaches that could have been employed. The use of 

regional modeling to generate PM2.5 species specific monthly averaged diurnal patterns would be 

unnecessary if continuous PM2.5 speciation monitoring were more readily available. Use of an 

algorithm to estimate PM10 light extinction would be unnecessary if direct measurements of 

continuous PM10 light extinction were commonly available. 

5.3 Visibility Conditions for Alternative Secondary PM NAAQS Scenarios 

Purpose: The goal is to evaluate the effectiveness of alternative secondary PM2.5

NAAQS, including 2 that use the PM2.5 mass concentration indicator (i.e., the current 15 μg/m3

annual, 35 μg/m3 24-hour PM2.5 NAAQS and a more restrictive 12 μg/m3/25 μg/m3 alternative), 

and 18 that use a 1-hour daylight PM10 light extinction indicator (i.e., all combinations of both a 

maximum daily 1-hr indicator and a 1-hr indicator based on all daylight hours with 3 percentile 

forms (90th, 95th and 98th) and 3 levels (CPLs) for the 15 urban areas). 

Approach: The hourly averaged PM10 light extinction data set developed to characterize 

current conditions for the 15 urban areas was used as the starting point for a rollback adjustment 

to simulate just meeting the various alternative PM NAAQS scenarios. Rollback is not applied 

to the PRB portion of the PM2.5 as estimated using CMAQ modeling, though it is applied 

uniformly to all other PM2.5 components. This process produces adjusted daylight hourly PM10

light extinction data sets for each urban area that would just meet each of the alternative NAAQS 

scenarios that can be assessed with respect to their visibility protection effectiveness. 

Principal Results: Each of the PM NAAQS scenarios that used PM10 light extinction as 

the indicator produced similar distributions of hourly PM10 light extinction across the 15 urban 

areas. This is not the case for the two scenarios that used PM2.5 mass concentration as the 

indicator, where for example the 90th percentile PM10 light extinction values vary among the 15 

urban areas by as much as a factor of 2 to 3. The maximum daily 1-hour form of the alternative 

NAAQS is more restrictive for any percentile than the all-hours form. In fact the 90th percentile 

of the maximum daily form is nearly identical with respect to design values for current 
5-5

conditions and the percent reduction required to just meet it as the 98th percentile for the allhours form. 

Technical Issues: The rollback approach implicitly assumes all non-PRB PM2.5

components will be uniformly reduced to meet any of the alternative standards. In practice, 

emission control programs that would be developed to meet standards will not operate in this 

manner, nor will they be so fine tuned that each urban area would just meet the PM NAAQS. In 

that sense, the rollback assessment produces idealized results which for the PM10 light extinction 

based NAAQS scenarios are more uniform across urban areas than is likely should such a 

standard be implemented. The use of this nationally uniform emissions rollback approach is 

justified by it providing a common basis for assessing the variations in the magnitude of 

emissions controls required to meet NAAQS scenarios for urban areas across the country. 
This page left intentionally blank. 
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5764900659b22!OpenDocument

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0BAPPENDICESF

1

A. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of 

PM10 Light Extinction in the 15 Study Areas 

B. Distributions of Estimated PM2.5 and Other Components under Current Conditions 

C. Development of PRB Estimates of PM2.5 Components, PM10-2.5, and PM10 Light 

Extinction 

D. Relationships between PM Mass Concentration and PM10 Light Extinction under 

Current Conditions 

E. Differences in Daily Patterns of Relative Humidity and PM10 Light Extinction between 

Areas and Seasons 

F. Distributions of Maximum Daily Daylight PM10 Light Extinction under “Just Meets” 

Conditions 

G. Additional Information on the Exclusion of Daylight Hours with Relative Humidity 

Greater than 90 Percent 

H. Inter-Year Variability 

I. Daylight Hours 

J. Logit Memorandum 

 

1

When viewing St. Louis results throughout these appendices, the reader should keep in mind that credible 

comments concerning unrealistically high PM10-2.5 values were received but too late in the review process to permit 

reanalysis using an alternate data set or to remove St. Louis from all portions of this document. However, the text in 

the body of this document has been revised to caution readers with respect to the St. Louis results, and they will not 

be included in the visibility effects discussion in the final PM Policy Assessment document. Some graphics have 

been updated to exclude St. Louis results. 
This page left intentionally blank. 
 A-1 

1BAPPENDIX A 

2BPM2.5 MONITORING SITES AND MONITORS PROVIDING 

2005-2007 DATA FOR THE ANALYSIS OF PM10 LIGHT 

EXTINCTION IN THE 15 STUDY AREAS 
 A-2 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Tacoma AQS ID HU530530029U

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - L 

STREET 

Address: 7802 SOUTH L 

STREET, TACOMA 

0.5 miles east of I-5 

2005-2007 annual DV = 10.2 

2005-2007 24-hr DV = 43 

This is the highest 24-hour PM2.5 

DV site in the Seattle-TacomaOlympia, WA annual PM2.5

nonattainment area 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-inthree sampling schedule) 

 PM2.5 speciation (one-in-six 

sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

Correlated Radiance Research 

M903 Nephelometry 

No continuous PM10 monitoring at 

this site, see right hand column..

NA AQS ID HU530530031U

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - ALEXANDER 

AVE 

Address: 2301 ALEXANDER AVE, TACOMA, 

WA 

6.4 miles NNE of PM2.5 site 

Neighborhood Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

 Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

 Sample Analysis Method: TEOMGRAVIMETRIC 

7% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-3 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Fresno AQS ID HU060190008U

State: California 

City: Fresno 

MSA: Fresno, CA 

Local Site Name: None given 

Address: 3425 N FIRST ST, 

FRESNO 

2.5 miles west of the airport, 3 

miles NNE of central Fresno 

2005-2007 annual DV = 17.4 

2005-2007 24-hr DV = 63 

This is not the highest annual or 

24-hr PM2.5 DV site in the San 

Joaquin nonattainment area. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) Met-One BAM 

No continuous PM10 monitoring at 

this site, see right hand column..

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper 
 A-4 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Los Angeles AQS ID HU060658001U

State: California 

City: Rubidoux (West Riverside) 

MSA: Riverside-San Bernardino, 

CA 

Local Site Name: None given 

Address: 5888 MISSION BLVD., 

RUBIDOUX 

Eastern SCAB, 0.4 miles from 

Pomona Freeway. 

2005-2007 annual DV = 19.6 

2005-2007 24-hr DV = 55 

This site is not the highest DV site 

in the LA-South Coast 

nonattainment area. 

Neighborhood scale. 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

R&P 1400 TEOM 

No continuous PM10 monitoring at 

this site, see right hand column..

NA AQS ID HU060710306U

State: California 

City: Victorville 

MSA: Riverside-San Bernardino, CA 

Local Site Name: MOVED FROM 060710014 

Address: 14306 PARK AVE., VICTORVILLE, 

CA 

36 miles north of PM2.5 site, on the other side 

of a range of hills. 0.4 miles from I-15 

Measurement Scale not given in AQS, but 

appears Neighborhood by aerial image. 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

 Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

 Sample Analysis Method: TEOMGRAVIMETRIC 

6% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-5 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Phoenix AQS ID HU040137020UH (FRM 

& CSN) 

State: Arizona 

City: Scottsdale 

MSA: Phoenix-Mesa, AZ 

Local Site Name: 

Address: 10844 EAST OSBORN 

ROAD SCOTTSDALE' AZ 

Reporting Agency: Salt River 

Pima-Maricopa Indian Community 

of Salt River Reservation 

Eastern edge of the metro area, 

largely surrounded by agricultural 

fields. 

2005-2007 annual DV = 7.9 

2005-2007 24-hr DV = 15 

This site is not the highest DV site 

in the Phoenix-Mesa CBSA. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-insix sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

No continuous PM10 monitoring at 

this site, see right hand column. 

AQS ID HU040139998UH

(Continuous) 

State: Arizona 

City: Phoenix 

MSA: Phoenix-Mesa, AZ 

Local Site Name: Vehicle 

Emissions Laboratory 

Address: 600 N 40th St & 

Fillmore St 

Measurement Scale not 

available; 0.75 miles from 

intersection of two 

freeways, 1 mile from 

Phoenix airport. 

Parameters taken from this 

site: 

 1-hour PM2.5 mass. 

Nephelometer. 

AQS ID HU040133002U

State: Arizona 

City: Phoenix 

MSA: Phoenix-Mesa, AZ 

Local Site Name: CENTRAL PHOENIX 

Address: 1645 E ROOSEVELT ST-CENTRAL 

PHOENIX STN 

1.8 miles NE of central Phoenix 

Neighborhood Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

Sample Collection Method: INSTRUMENTALR&P SA246B-INLET 

Sample Analysis Method: TEOMGRAVIMETRIC 

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-6 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Salt Lake City AQS IDHU490353006U

State: Utah 

City: Salt Lake City 

MSA: Salt Lake City-Ogden, UT 

Local Site Name: UTM 

COORDINATES = PROBE 

LOCATION 

Address: 1675 SOUTH 600 EAST, 

SALT LAKE CITY 

2.5 miles SSE of central Salt Lake 

City 

2005-2007 annual DV = 10.7 

2005-2007 24-hr DV = 48 

This is not the highest DV site in 

the Salt Lake City CSA. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) FDMS-Gravimetric 

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

Dallas AQS ID HU481130069U

State: Texas 

City: Dallas 

MSA: Dallas, TX 

Local Site Name: DALLAS 

HINTON 

Address: 1415 HINTON STREET 

4.5 miles NE of central Dallas 

2005-2007 annual DV = 11.5 

2005-2007 24-hr DV = 25 

This is not the highest DV site in 

the Dallas-Ft. Worth CSA. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C 

No continuous PM10 monitoring at 

this site, see right hand column..

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-7 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Houston AQS ID HU482010024U

State: Texas 

City: Not in a city 

MSA: Houston, TX 

Local Site Name: HOUSTON 

ALDINE 

Address: 4510 1/2 ALDINE MAIL 

RD 

10 miles NNE of central Houston 

2005-2007 annual DV = 13.1 

2005-2007 24-hr DV = 25 

This is not the highest DV site in 

the 'Houston-Baytown-Huntsville, 

TX CSA. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-in-six 

day sampling schedule) 

 PM2.5 speciation (one-in-six 

sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C 

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-8 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

St. Louis AQS ID HU295100085U

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: BLAIR STREET 

CATEGORY A CORE SLAM 

PM2.5. 

Address: BLAIR S 

2 miles north of central St. Louis 

2005-2007 annual DV = 14.5 

2005-2007 24-hr DV = 34 

This is not the highest DV site in 

the St. Louis nonattainment area. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 30 deg C 

No continuous PM10 monitoring at 

this site, see right hand column.

NA AQS ID HU295100092UH (2005 and 2006 

data) 

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: 

Address: 3 NORTH MARKET 

0.7 miles ESE of PM2.5 site, across the street 

from the eastern edge of what appears to be 

a recycling/municipal works yard. 

Middle Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

 Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

 Sample Analysis Method: TEOMGRAVIMETRIC Site was on the other 

(western) side of the recycling/municipal 

works yard as site 295100093, below. 

HU295100093UH (2007 data) 

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: None given 

Address: Branch Street 

0.6 miles ESE of PM2.5 site, across the street 

from the western edge of what appears to be 

a recycling/municipal works yard. 

Middle Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

 Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

 Sample Analysis Method: TEOMGRAVIMETRIC 

 

4% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-9 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Birmingham AQS ID HU010730023U

State: Alabama 

City: Birmingham 

MSA: Birmingham, AL 

Local Site Name: 

Address: NO. B'HAM,SOU R.R., 

3009 28TH ST. NO 

2.3 miles north of central 

Birmingham 

2005-2007 annual DV = 18.7 

2005-2007 24-hr DV = 44 

This is the highest DV site in the 

Birmingham nonattainment area 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 50 deg C 

 1-hour PM10 STP mass (AQS 

parameter 81102) 

Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

Sample Analysis Method: TEOMGRAVIMETRIC 

NA Same as PM2.5 site. 

0.3% of PM10-2.5 values were determined 

using regional average PM10-2.5: PM2.5 ratios 

from 2005 Staff Paper
 A-10 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Atlanta AQS ID HU130890002U

State: Georgia 

City: Decatur 

MSA: Atlanta, GA 

Local Site Name: 2390-B 

WILDCAT ROAD, DECATUR, GA 

Address: SOUTH DEKALB 

About 7 miles SE of central Atlanta 

2005-2007 annual DV = 15.7 

2005-2007 24-hr DV = 33 

This is not the highest DV site in 

the Atlanta nonattainment area. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 30 deg C 

No continuous PM10 monitoring at 

this site, see right hand column.

NA AQS ID HU131210048U

State: Georgia 

City: Atlanta 

MSA: Atlanta, GA 

Local Site Name: Georgia Tech, Ford 

Environmental Science and Technology Bldg, 

roof 

Address: GA. TECH., Ford ES&T Bldg, 311 

Ferst St NW, Atlanta GA 

8.6 miles NW of PM2.5 site 

Neighborhood Scale 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

Sample Collection Method: INSTRUMENT 

MET ONE 4 MODELS 

Sample Analysis Method: BETA 

ATTENUATION 

8% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-11 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Detroit AQS ID HU261630033U

State: Michigan 

City: Dearborn 

MSA: Detroit, MI 

Local Site Name: PROPERTY 

OWNED BY DEARBORN PUBLIC 

SCHOOLS 

Address: 2842 WYOMING 

About 0.2 miles from Ford River 

Rouge auto plant 

2005-2007 annual DV = 17.2 

2005-2007 24-hr DV = 43 

This is the highest annual and 24-

hr DV site in the Detroit 

nonattainment area 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-in-six 

sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) TEOM Gravimetric 50 deg C 

 1-hour PM10 STP mass (AQS 

parameter 81102) 

Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

Sample Analysis Method: TEOMGRAVIMETRIC

NA Same as PM2.5 site. 

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-12 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Pittsburgh AQS ID HU420030008U

State: Pennsylvania 

City: Pittsburgh 

MSA: Pittsburgh, PA 

Local Site Name: None given 

Address: BAPC 301 39TH 

STREET BLDG #7 

3 miles NE of central Pittsburgh, 

0.5 miles from Allegheny River 

2005-2007 annual DV = 15.0 

2005-2007 24-hr DV = 40 

This site is not the highest DV site 

in the Pittsburgh nonattainment 

area. 

Urban Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C 

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

Baltimore AQS ID HU240053001UH (FRM 

& CSN) 

State: Maryland 

City: Essex 

MSA: Baltimore, MD 

Local Site Name: Essex 

Address: 600 Dorsey Avenue 

7 miles east of central Baltimore 

2005-2007 annual DV = 14.5 

2005-2007 24-hr DV = 35 

This is not the highest DV site in 

the Baltimore nonattainment area. 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM10 LC mass (AQS 

parameter 85101 

AQS ID HU245100040UH

(Continuous) 

State: Maryland 

City: Baltimore 

MSA: Baltimore, MD 

Local Site Name: Oldtown 

Address: Oldtown Fire 

Station, 1100 Hillen Street 

1 mile NNE of Inner Harbor 

area 

Middle Scale 

Parameters taken from this 

site: 

 1-hour PM2.5 mass 

(AQS parameter 88502, 

Acceptable PM2.5 AQI & 

Speciation Mass) TEOM 

Gravimetric 50 deg C 

Same as PM2.5 site. 

5% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-13 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Philadelphia AQS IDHU100032004UH (DE) 

State: Delaware 

City: Wilmington 

MSA: Wilmington-Newark, DE-MD 

Local Site Name: CORNER OF 

MLK BLVD AND JUSTISON ST 

2.5 miles NE of central 

Wilmington, 0.25 miles from the 

Delaware River, 22 miles SW from 

central Philadelphia 

2005-2007 annual DV = 14.7 

2005-2007 24-hr DV = 37 

This is not the highest DV site in 

the Philadelphia nonattainment 

area 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-in-six 

sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) Beta Attenuation 

 1-hour PM10 STP mass (AQS 

parameter 81102) 

NA Same as PM2.5 site. 

3% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

New York AQS ID HU340390004UH (NJ) 

State: New Jersey 

City: Elizabeth 

MSA: Newark, NJ 

Local Site Name: ELIZABETH 

LAB 

Address: NEW JERSEY 

TURNPIKE INTERCHANGE 13 

1.75 miles south of Elizabeth, at 

the I-95 interchange with I-278 

2005-2007 annual DV = 14.4 

2005-2007 24-hr DV = 42 

This is not the highest DV site in 

the New York nonattainment area 

Neighborhood Scale 

Parameters taken from this site: 

 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule) 

 PM2.5 speciation (one-inthree sampling schedule) 

 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 30 deg C 

No continuous PM10 monitoring at 

this site, see right hand column. 

NA AQS ID HU360610125U

State: New York 

City: New York 

MSA: New York, NY 

Local Site Name: PARK ROW 

Address: 1 PACE PLAZA 

Near the on-ramp to the Brooklyn Bridge, 

Manhattan end 

Measurement scale not stated. 

Parameters taken from this site: 

 1-hour PM10 STP mass (AQS parameter 

81102) 

Sample Collection Method: INSTRUMENTALR&P SA246B-INLET 

Sample Analysis Method: TEOMGRAVIMETRIC 

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
 A-14 

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM10 Light Extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable) 

PM10 data source for PM10-2.5

Notes: 

 In this Table, the 1-hour concentration parameter “88502, Acceptable PM2.5 AQI & Speciation Mass” is the 

same as the ISA refers to as “FRM-like” PM2.5 mass. An entry of “88501, PM2.5 Raw Data” indicates that 

the monitoring agency makes no representation as to the degree of correlation with FRM PM2.5 mass. The 

latter type of continuous PM2.5 data were used only when the former were unavailable. 

 Where PM10 was reported in STP, it was converted to LC before PM10-2.5 was calculated. 

 All continuous PM2.5 data were obtained through the AirNow data system rather than from AQS, as an 

initial exploration indicated that not all the desired 1-hour data from all sites had been submitted to AQS. 

Data are submitted to the AirNow system within hours of collection and may not be subject to as much data 

validation review as is typical for data in AQS, despite the opportunity offered by the AirNow system for 

monitoring agencies to correct data after initial submission. 
 B-1

3BAPPENDIX B 

4BDISTRIBUTIONS OF ESTIMATED PM2.5 AND OTHER 

COMPONENTS 
B-2

Figure B-1 – Distribution of Daily Maximum PM2.5 and PM10-2.5 Across the 2005-2007 Period, by 

(a) Daily Maximum Daylight PM2.5
B-3

(b) Daily Maximum Daylight PM10-2.5 
B-4

Figure B-2 – Distribution of Hourly PM2.5 Components Across the 2005-2007 Period, by Study Area

(a) 1-Hour Daylight Sulfate (dry, fully neutralized) 
B-5

Figure B-2 – Distribution of Hourly PM2.5 Components Across the 2005-2007 Period, by Study Are

(b) 1-Hour Daylight Nitrate (dry, fully neutralized, CSN method consistent) 
B-6

Figure B-2 – Distribution of Hourly PM2.5 Components Across the 2005-2007 Period, by Study Are

(c) 1-Hour Daylight Elemental Carbon 
B-7

Figure B-2 – Distribution of Hourly PM2.5 Components Across the 2005-2007 Period, by Study Are

(d) 1-Hour Daylight Organic Carbonaceous Material (by SANDWICH method) 
B-8

Figure B-2 – Distribution of Hourly PM2.5 Components Across the 2005-2007 Period, by Study Are

(e) 1-Hour Daylight Fine Soil 
C-1

5BAPPENDIX C 

6BDEVELOPMENT OF PRB ESTIMATES OF PM2.5

COMPONENTS, PM10-2.5, AND PM10 LIGHT EXTINCTION 

Policy relevant background levels of PM10 light extinction have been estimated for 

this assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic 

emission in the U.S., Canada, and Mexico were omitted, as described in the ISA (US EPA, 

2009a). Estimates of PRB for PM10 light extinction were calculated from modeled 

concentrations of PM2.5 components using the IMPROVE algorithm. The necessary 

component concentrations were extracted from the CMAQ output files, as they were not 

summarized in the ISA (US EPA, 2009a). 

More specifically, for each study area, EPA staff overlaid CMAQ grid cells over 

shapes representing the Census-defined urbanized area for each study area, and visually 

identified the CMAQ grid cells that had a substantial portion of their area coincident with the 

urbanized area. For each such grid cell, for each of the 12 months of the year, we obtained 

the 24 values of the hour-specific average concentrations of the five PM2.5 components. We 

then averaged these across the selected grid cells. Thus, a given hour of the day has the same 

PRB estimate for a component on all days within a month, but months and study areas differ. 

We generally observed that PRB concentrations did not vary greatly across the several grid 

cells overlaying the urbanized area of a given study area; this is reasonable given the 

exclusion of local anthropogenic sources from this CMAQ model run. CMAQ estimates of 

PRB for the five PM2.5 components averaged across grid cells and months were not adjusted 

in any.F

2

There are too many values of PRB to present or illustrate them comprehensively in 

this document. Table C-1 presents annual average concentrations by study area to 

summarize these PRB estimates for the PM2.5 components (including the specific form 

assumed for sulfate, nitrate, and organic carbon). The right hand column of the table shows 

the PM2.5 mass calculated from the CMAQ-estimated components, including factors to fully 

neutralize sulfate and nitrate (but with no water mass added). One notable feature of the 

annual average of the PRB estimates is the relatively high values for elemental and organic 

carbon PRB for the Tacoma study area. This area is often affected by wildfires for extended 

 

2

 This approach to estimation of PRB for PM2.5 shares the same information source but is more 

disaggregated than the approach used in the health risk assessment for this review of the PM NAAQS (US EPA, 

2010e). In the health risk assessment, PRB estimates for PM2.5 mass concentration are taken from the same CMAQ 

model run, but are averaged by calendar quarter and by region of the country. 
C-2

periods in the autumn months, and such fires were included in the 2004 emissions scenario 

for the PRB CMAQ run. A cursory review of information on fire events in 2005-2007 

confirmed that the fire situation in this part of the country in 2004 was not an anomaly. 

Another notable feature of the PRB estimates is that the values for nitrate and fine 

soil/crustal are low relative to previous estimates of natural background concentrations of 

these fine PM components in Class I areas. These previous estimates by Trijonis (1990), 

repeated in the 2003 EPA guidance document “Guidance for Estimating Natural Visibility 

Conditions Under the Regional Haze Program” (US EPA, 2003), are 0.10 μg/m3 for 

(neutralized) nitrate and 0.50 μg/m3 for fine soil. These estimates are based largely on data 

from the earliest of the IMPROVE monitoring stations, and thus may include some influence 

from non-PRB emissions. On the other hand, it is understandable that the unadjusted output 

from the PRB CMAQ scenario would underestimate nitrate and fine soil. CMAQ is known 

to underestimate actual nitrate in many situations when provided with a complete NOx 

inventory, and the nonanthropogenic emission inventory for NOx itself has uncertainties. 

The non-anthropogenic emission inventory for the PRB CMAQ run may also quite easily 

underestimate nonanthropogenic emissions of fine soil. However, even if the estimates for 

PRB nitrate and fine soil were increased to match the Trijonis (1990) estimates, the resulting 

values for PRB PM10 light extinction would increase only a little. Even at 90 percent relative 

humidity, the contribution to PM10 light extinction calculated from the Trijonis estimates is 

1.7 Mm-1, versus the average of about 0.5 Mm-1 using the estimates in Table C-1. The 

increment of 1.2 Mm-1 would be only about 10 to 20 percent of the PRB PM10 light 

extinction estimates shown in Table C-4, and would not significantly affect the calculation of 

PM10 light extinction values under the “what if” scenarios. 
C-3

Table C-1. Summary of PRB Estimates for the Five PM2.5 Components: Average 1-Hour 

Values Across 2005-2007 

Study Area Average 1-Hour PRB Concentration Across 2005-2007 (μg/m3)

Sulfate 

 (dry, no 

ammonium)

Nitrate 

(dry, no 

ammonium) 

Elemental 

Carbon

Organic 

Carbonaceous 

Material 

Fine 

Soil/Crustal

Calculated 

PM2.5

Tacoma 0.45 0.026 0.15 1.3 0.31 2.4 

Fresno 0.4 0.00062 0.08 0.74 0.19 1.6

Los Angeles 0.36 0.0037 0.028 0.3 0.036 0.9

Phoenix 0.31 0.000052 0.02 0.26 0.015 0.7

Salt Lake City 0.25 0.00028 0.025 0.26 0.034 0.7

Dallas 0.27 0.0022 0.055 0.59 0.092 1.1

Houston 0.3 0.0055 0.091 0.86 0.17 1.5

St. Louis 0.31 0.0027 0.047 0.53 0.07 1.1

Birmingham 0.29 0.007 0.099 1.1 0.19 1.8

Atlanta 0.3 0.016 0.1 1.1 0.19 1.8

Detroit-Ann 0.34 0.00062 0.024 0.32 0.018 0.8

Pittsburgh 0.3 0.00052 0.029 0.36 0.034 0.8

Baltimore 0.34 0.0016 0.039 0.44 0.054 1.0

Philadelphia 0.34 0.00097 0.03 0.36 0.032 0.9

New York City 0.36 0.0038 0.026 0.31 0.022 0.9 

Average 0.33 0.00 0.06 0.59 0.10 1.20 
C-4

It is also necessary to have estimates of PRB for PM10-2.5, to feed into the IMPROVE 

algorithm. It is not EPA’s practice to rely on coarse PM estimates from CMAQ modeling, so 

other sources of PRB estimates were considered. The final ISA for this review does not 

present any new information on this subject. The approach used in the previous two Criteria 

Documents was to present the historical range of annual means of PM10-2.5 concentrations 

from IMPROVE monitoring sites selected as being least influenced by anthropogenic 

emissions. See Table 3E-1 of the 2004 Criteria Document (reproduced here as Table C-3). 

For sites in the lower 48 states, these annual means ranged from a low of 1.8 μg/m3 to a high 

of 10.8 μg/m3. No cross-year average or median values were provided that could be used as 

the point estimates needed in this assessment. Therefore, for this assessment, EPA staff 

estimated PRB for PM10-2.5 using a contour map based on average 2000-2004 PM10-2.5

concentrations from all IMPROVE monitoring sites, found in a recent report from the 

IMPROVE program (DeBell, 2006). We located each study area’s position on this map, and 

assigned it the mid-point of the range of concentrations indicated by the contour band for that 

location. The contour map is reproduced here as Figure C-1. Stars show locations of the 15 

study areas. In this reproduction, the midpoints of the contour ranges have been added to the 

legend. 

The results for PRB for coarse PM are shown in Table C-2. Lacking any other 

information, these PRB values are taken to apply to every hour of the year. The contour map 

and thus these values are influenced by data from IMPROVE sites that were not considered 

in the 2004 Criteria Document because they are not sufficiently isolated from the influence of 

anthropogenic emissions, including three IMPROVE sites in urban areas which clearly are 

influenced by anthropogenic emissions, and thus may be overestimates of PRB for coarse 

PM. Nevertheless, these values are generally within the range of values presented in the 

Criteria Document for the more isolated sites. These values for the more isolated sites are 

reproduced here in Table C-3 for ease of comparison. Further, these PRB values are low 

enough that their exact values have little effect on the results of “what if” estimation of PM10

light extinction levels under possible secondary PM NAAQS. 

Table C-4 presents the resulting 2005-2007 average PRB daylight PM10 light 

extinction by study area, determined by using each daylight hour’s f(RH),F

3

F the hour-specific 

PRB PM2.5 component estimates (summarized only as annual averages in Table C-1), the 

PRB PM10-2.5 estimates in Table C-2, and the IMPROVE algorithm. The sulfate and nitrate 

component values in Table C-1 are multiplied by 1.375 and 1.29 to reflect full neutralization, 

before being used in the IMPROVE algorithm. While for conciseness Table C-4 presents 

 

3

 Hour-specific relative humidity for PRB conditions was assumed to be the same as measured for current 

conditions. 
C-5

only the annual average PRM for PM10 light extinction for all daylight hours in 2005-2007 in 

the rollback analysis of “what if” conditions hour-specific PRB values are retained and used. 

The values of PRB PM10 light extinction in Table C-4 range between 5 and 11 Mm-1. 

For comparison, the default estimates of natural visibility conditions in the 2003 EPA 

guidance document for Class I areas range between about 15 and 20 Mm-1, including the 

Rayleigh contribution of about 10 Mm-1. Thus, on an annual average basis the range of PRB 

estimates for PM10 light extinction used for this assessment is very consistent with the range 

of total light extinction values recommended in the guidance document. 
C-6

Figure C-1. Selection of PRB Values for PM10-2.5 Based on Contoured IMPROVE 

Monitoring Data 

9.5

8.5

7.5

6.5

5.5

4.5

3.5

2.5

1.5

0.5

9.5

8.5

7.5

6.5

5.5

4.5

3.5

2.5

1.5

0.5
C-7

Table C-2. Policy Relevant Background Concentrations of PM10-2.5 Used in This 

Assessment, Based on Measurements at IMPROVE Sites 

Study Area PRB PM10-2.5 Mass (μg/m3) 

Tacoma 4.5 

Fresno 5.5 

Los Angeles 4.5 

Phoenix 5.5 

Salt Lake City 4.5 

Dallas 8.5 

Houston 5.5 

St. Louis 7.5 

Birmingham 5.5 

Atlanta 5.5 

Detroit 9.5 

Pittsburgh 3.5 

Baltimore 3.5 

Philadelphia 6.5 

New York 3.5 

Table C-3. Ranges of 1990-2002 Annual Mean PM Concentrations at IMPROVE 

Monitoring Sites (μg/m3

) 

Source: Table 3E-1 of the 2004 Air Quality Criteria Document for PM (US EPA, 2004) 
C-8

Table C-4. 2005-2007 Average Policy Relevant Background Daylight PM10 Light 

Extinction 

 

Study Area 

2005-2007 Average Policy Relevant 

Background Daylight PM10 Light Extinction, 

Mm-1

Tacoma 11 

Fresno 11 

Los Angeles 9 

Phoenix 8 

Salt Lake City 5 

Dallas 8 

Houston 10 

St. Louis 9 

Birmingham 9 

Atlanta 10 

Detroit 7 

Pittsburgh 7 

Baltimore 8 

Philadelphia 8 

New York 8 
D-1

7BAPPENDIX D 

8BRELATIONSHIPS BETWEEN PM MASS 

CONCENTRATION AND PM10 LIGHT EXTINCTION 

UNDER CURRENT CONDITIONS 

In the last review, the 2005 Staff Paper (US EPA, 2005) examined the correlation 

between PM10 light extinction and PM2.5 mass concentrations, each defined for various 

consistent time periods. The 2005 Staff Paper analysis assumed that the percentage mix of PM2.5

components was the same in all 24 hours of each day, equal to that indicated by 24-hour CSN 

sampling. The modeling of 1-hour PM10 light extinction in this new assessment allows these 

correlations to be re-examined, with the more realistic treatment in which the mix of PM2.5

components is modeled to vary during the day, based in part on diurnal profiles from CMAQ 

modeling (see section 3.2.2). 

Five scatter plot figures relating PM2.5 mass concentrations and PM10 light extinction are 

presented here for the individual study areas, using different time periods for the two parameters; 

these time periods are not always matched. In each figure, the solid red curve was estimated by 

applying locally weighted scatter plot smoothing (LOESS) to the data. LOESS is a form of 

locally weighted polynomial regression (see Hhttp://support.sas.com/rnd/app/papers/loesssugi.pdfH) 

and is a convenient way to visualize whether a dense data cloud in a scatter plot reflects a more 

linear or more nonlinear relationship. The LOESS results in each case indicate a generally linear 

relationship as a central tendency but with considerable variability around that central tendency. 

Table D-1 presents squared correlation coefficients between observed and LOESS modelpredicted values for all five figures. Because the LOESS regressions are generally linear, 

comparisons among these correlation coefficients should lead to the same qualitative conclusions 

as if coefficients from linear regressions were compared. All values of PM10 light extinction 

presented here are based on excluding daylight hours with relative humidity greater than 90 

percent; hence, a nominally 4-hour period might have as few a one 1-hour PM10 light extinction 

value, although this is rare in this data set (see the tile plots in Figure 3-12). However, values of 

PM2.5 mass concentration do not exclude any hours within the time period specified. Note that if 

several study areas were grouped by region and combined into a single scatter plot and LOESS 

fit, similar to the analysis of this topic in the 2005 Staff Paper, the correlations would be weaker 

than observed here for individual study areas. 

Figure D-1 compares 24-hour PM2.5 mass (as measured by the FRM/FEM filter-based 

sampler) to daily maximum daylight PM10 light extinction. The scatter is due the variations in 
D-2

PM2.5 concentration, in the mix of PM2.5 components, and in relative humidity during the day 

and across days. Variations in PM10-2.5 concentrations also contribute to the scatter, in all five 

comparisons presented here, since very high levels of PM10-2.5 substantially influence PM10 light 

extinction. This source of variability in the scatter plots is particularly important for Los 

Angeles, Phoenix, and St. Louis which have many (Phoenix) or some (Los Angeles and St. 

Louis) hours with high PM10-2.5. 

Because of the large scatter and low correlation coefficients when using 24-hour PM2.5

mass concentration to predict daily maximum daylight PM10 light extinction, it is natural to 

investigate how much the correlation improves when the PM2.5 mass indicator is limited to 

shorter periods of time. The next four figures investigate correlations during such shorter 

periods, both matched and un-matched in time. 

Figure D-2 compares hourly PM2.5 mass (as actually measured by the continuous 

instruments) vs. same-hour daylight PM10 light extinction. Lack of agreement due to mismatch 

of time period is not a factor in this comparison. However, there is still considerable scatter due 

to variations in the mix of PM2.5 components and in relative humidity across hours and days. In 

addition, continuous PM2.5 mass instruments do not register the mass of each component 

consistently with FRM/FEM and CSN samplers and lab analysis methods. This affects the 

scatter in this figure because the estimates of hourly PM10 light extinction are linked to the 

FRM/FEM and CSN measurements more strongly than to the continuous PM2.5 measurements. 

Note that the correlation values in Table D-1 for this comparison are better than those for the 24-

hour comparison in most but not all study areas. An implication of this figure and the 

information in Table D-1 is that a wide range of PM10 light extinction levels can prevail in hours 

that have the same PM2.5 mass concentration, even at a single site. Additional variability no 

doubt exists across areas. 

Figure D-3 compares 12-4 pm average PM2.5 mass vs. 12-4 pm average PM10 light 

extinction. The 2005 Staff Paper observed that because this time period is generally the time of 

lowest relative humidity, the relationship between PM2.5 mass and PM10 light extinction (i.e., the 

ratio of the two or the slope of the regression line) is more uniform across areas during this 

period than the relationship for values of each averaged over all 24 hours in a day. In addition, 

the longer averaging period might be expected to reduce the effect of variability in the 

measurement of hourly PM2.5 mass. However, comparison of Figures D-2 (time-matched single 

hours) and D-3 (time-matched 4 afternoon hours) and the corresponding columns of Table D-1 

indicates that, after exclusion of hours with relatively humidity greater than 90 percent, the 

scatter in Figure D-3 is about the same as in Figure D-2. This residual scatter is due to 

composition differences from hour-to-hour, as well as to variations in relative humidity during 

hours with relative humidity of 90 percent or less. It can also be observed by comparing Figures 
D-3

D-2 and D-3 that the period between 12 pm and 4 pm generally has lower levels of PM10 light 

extinction than for all daylight hours taken together, even after the exclusion of the hours with 

the highest relative humidity. (Note the change in scale between these two figures.) 

Figure D-4 compares 12-4 pm average PM2.5 mass vs. daily maximum daylight 1-hour 

PM10 light extinction. This time-unmatched comparison tests the usefulness of a 12-4 pm PM2.5

mass indicator as a predictor of the daily PM10 light extinction metric of potentially greatest 

interest. The scatter in Figure D-4 is typically more than in Figure D-3 (4 time-matched 

afternoon hours), because daily maximum daylight 1-hour PM10 light extinction often occurs 

earlier in the day than the 12-4 pm period used to average the PM2.5 mass, and the time period 

mismatch introduces prediction errors due to changes in PM2.5 concentration and composition 

and relative humidity. An implication is that while a secondary NAAQS based on 12-4 pm 

average PM2.5 mass might achieve a given level of protection across days and areas in avoiding 

high levels of PM10 light extinction between 12 and 4 pm, with some variation across areas due 

to composition and relative humidity differences, there could be considerable additional variation 

in the level of protection against PM10 light extinction during the earlier hours of the day when 

some areas often have their highest PM10 light extinction levels. 

Figure D-5 compares 8 am-12 pm average PM2.5 mass vs. daily maximum daylight 1-

hour PM10 light extinction. This comparison is of interest because it may reduce the number of 

instances of time mismatch, versus the comparison made in Figure D-4, if the daily maximum 

PM10 light extinction often occurs between 8 am and 12 pm. The scatter in Figure D-5 is 

typically less than in Figure D-4 and the squared correlation coefficients larger, indicating that 

this earlier averaging period for PM2.5 mass more often encompasses the period of maximum 

PM10 light extinction. However, the scatter in Figure D-5 is greater than that in Figure D-3 (4 

time-matched afternoon hours). 

Figure D-6 provides another perspective on the possible use of PM2.5 mass concentration 

as an indicator for a secondary PM NAAQS aimed at protecting visual air quality. Figure D-6 

shows in box-and-whisker plot form two versions of the ratios of PM10 light extinction to PM2.5

mass concentration, allowing a comparison across the 15 study areas of the central tendencies 

and the distributions of these ratios. The Panel A version corresponds to the comparison in 

Figure D-1 (24-hour averages of PM2.5 mass and PM10 light extinction) and the Panel B version 

corresponds to the comparison in Figure D-2 (time-matched single hour values). The data points 

in Figure D-6 were prepared as follows. In each day for each study area, the value of the 

indicated PM10 light extinction (24-hour average or 1-hour value) was divided by the indicated 

PM2.5 concentration metric (24-hour average or 1-hour value). Ratios that reflect PM2.5

concentrations less than 5 μg/m3 or PM10 light extinction less than 64 Mm-1 were eliminated 

before plotting, as such data points represent days or hours that could not play any role in 
D-4

determining compliance with any of the NAAQS scenarios considered in this assessment; also, 

some of these low-concentration/extinction data pairs produced extreme ratios that obscured the 

pattern for data pairs of most policy interest. The maximum ratio value for the vertical scale in 

these plots is set at 40 to allow closer examination of the portion of the plot representing the bulk 

of the data; this prevents a very small number of daily maximum data points for a few study 

areas from appearing in Panel A and a very small percentage of 1-hour data points for a few 

study areas (Los Angeles and St. Louis in particular) from appearing in Panel B. The notable 

variation in the vertical positions of the 25-75 percentile boxes and the 90 percentile whiskers 

representing the ratios in the 15 areas illustrates the point that because of differences in PM 

composition mix and relative humidity (even after excluding hours with relative humidity greater 

than 90 percent) across study areas, a secondary NAAQS based on PM2.5 mass concentration 

would not give equal protection in terms of PM10 light extinction levels across cities, days, and 

hours. 

In the first public review draft of this assessment, it was notable that the correlation 

values for St. Louis and Philadelphia were much lower than for other areas. In this version 

(reflecting both corrections to relative humidity inputs and exclusion of hours with very high 

relative humidity) the correlation value for Philadelphia is about that for other eastern areas. The 

correlation values for St. Louis remain notably low relative to the average of all areas, for all five 

scatter plots. This is likely due to the influence of the high estimated values for PM10-2.5. In 

several other cases of notably low correlation, the small available range of PM2.5values relative 

to other areas contributes to the lower correlation values, e.g., in Phoenix, Dallas, and Houston. 
D-5

Table D-1. Squared Correlation Coefficients between Observed and LOESS 

Model-predicted Values of PM10 Light Extinction 

Area

Figure D-1 

24-Hour 

PM2.5 Mass 

vs. Daily 

Maximum 

Daylight 1-

Hour PM10

Light 

Extinction 

Figure D-2 

1-Hour 

PM2.5 Mass 

vs. SameHour PM10

Light 

Extinction 

Figure D-3 

12-4 pm 

Average 

PM2.5 Mass 

vs. 12-4 pm 

Average 

PM10 Light 

Extinction 

Figure D-4 

12-4 pm 

Average 

PM2.5 Mass 

vs. Daily 

Maximum 

Daylight 1-

Hour PM10

Light 

Extinction 

Figure D-5 

8 am-12pm 

Average 

PM2.5 Mass 

vs. Daily 

Maximum 

Daylight 1-

Hour PM10

Light 

Extinction 

Tacoma 0.48 0.80 0.78 0.29 0.65

Fresno 0.76 0.83 0.90 0.69 0.83

Los Angeles 0.57 0.63 0.66 0.52 0.69

Phoenix 0.22 0.67 0.73 0.18 0.20

Salt Lake City 0.88 0.89 0.95 0.80 0.89

Dallas 0.45 0.59 0.54 0.20 0.36

Houston 0.46 0.61 0.62 0.20 0.30

St. Louis 0.40 0.43 0.20 0.18 0.36

Birmingham 0.61 0.81 0.78 0.34 0.44

Atlanta 0.54 0.72 0.80 0.40 0.70

Detroit 0.62 0.55 0.61 0.11 0.30

Pittsburgh 0.73 0.63 0.66 0.52 0.62

Baltimore 0.78 0.69 0.69 0.58 0.71

Philadelphia 0.61 0.61 0.57 0.39 0.50

New York 0.69 0.77 0.76 0.51 0.62

AVERAGE 0.59 0.68 0.68 0.39 0.54 
D-6

Figure D-1. Relationship Between 24-Hour PM2.5 Mass vs. Daily Maximum Daylight 1-Hour PM
D-7

Figure D-2. Relationship Between Daylight 1-Hour PM2.5 Mass vs. Same-Hour PM10 Light Ex
D-8

Figure D-3. Relationship Between 12-4 pm Average PM2.5 Mass vs. 12-4 pm Average PM10 Light 
D-9

Figure D-4. Relationship Between 12-4 pm Average PM2.5 Mass vs. Daily Maximum Daylight 1

Extinction. 
D-10

Figure D-5. Relationship Between 8 am-12 pm Average PM2.5 Mass vs. Daily Maximum Daylight 1-H

Extinction 
D-11

Figure D-6. Distribution of Ratios of 1-Hour PM10 Light Extinction and PM2.5 Mass 

Concentration. 

A – Ratios of Daily Maximum Daylight 1-Hour PM10 Light Extinction to 24-Hour Average 

PM2.5 Concentration.
D-12

B – Ratios of Daylight 1-Hour PM10 Light Extinction to Same-Hour PM2.5 Concentration 
E-1

9BAPPENDIX E 

10BDIFFERENCES IN DAILY PATTERNS OF RELATIVE 

HUMIDITY AND PM10 LIGHT EXTINCTION BETWEEN 

AREAS AND SEASONS 

In the last review of the secondary PM NAAQS, the pattern of PM10 light extinction 

during the day was of particular interest. It was noted, using estimates of hourly PM10 light 

extinction based on a simpler approach than described for this analysis, that both (1) mid-day 

PM10 light extinction and (2) the slope of the relationship between PM10 light extinction and 

PM2.5 concentration varied less among regions of the country that at other times of the day. 

This was attributed to greater homogeneity of relative humidity across regions in the mid-day 

period. This is in contrast to the situation in the morning and later afternoon hours, when 

more eastern areas typically experience higher relative humidity levels than the more arid 

western and southwestern areas. The current analysis allows these patterns to be reexamined. 

Figures E-1 through E-4 show the diurnal pattern of season-average, hour-specific 

PM10 light extinction and relative humidity for the four “daylight seasons.” These graphics 

exclude hours with relative humidity greater than 90 percent. Light extinction and relative 

humidity for a given clock hour are averaged across the days in the season, across all three 

years. Daylight hours (per the simplified schedule of Table 3-5) are indicated by solid 

circles. Average 1-hour PM10 light extinction generally is highest in the morning, 

corresponding to higher relatively humidity (mostly due to lower temperature), higher 

vehicle traffic, and less dispersive conditions than later in the day. As was observed in the 

last review, there is more variation in average 1-hour PM10 light extinction among areas in 

the morning than at mid-day, although the morning variation has been reduced (relative to 

same information in the first public review draft of this assessment) by the exclusion of hours 

with relative humidity greater than 90 percent. 
E-2

Figure E-1. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10 Light Extinction (Mm

(a) November-January 
E-3

Figure E-2. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10 Light Extinction (M

continued 

(b) February-April 
E-4

Figure E-3. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10 Light Extinction (M

continued 

(c) May-July 
E-5

Figure E-4. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10 Light Extinction (M

continued 

(d) August-October 
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F-1 

1BAPPENDIX F 

12BDISTRIBUTIONS OF MAXIMUM DAILY AND HOURLY 

DAYLIGHT PM10 LIGHT EXTINCTION - UNDER “JUST 

MEET” CONDITIONS
F-2 

(a) NAAQS Scenario 

Daily Max 

191 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-3 

(b) NAAQS Scenario 

Daily Max 

191 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-4 

(c) NAAQS Scenario 

Daily Max 

191 Mm-1

 98th percentile 

 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-5 

(d) NAAQS Scenario 

Daily Max 

112 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-6 

(e) NAAQS Scenario 

Daily Max 

112 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-7 

(f) NAAQS Scenario 

Daily Max 

112 Mm-1

 98th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-8 

(g) NAAQS Scenario 

Daily Max 

64 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-9 

(h) NAAQS Scenario 

Daily Max 

64 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-10 

(i) NAAQS Scenario 

Daily Max 

64 Mm-1

 98th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-11 

(j) NAAQS Scenario 

All hours 

191 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-12 

(k) NAAQS Scenario 

All hours 

191 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-13 

(l) NAAQS Scenario 

All hours 

191 Mm-1

 98th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-14 

(m) NAAQS Scenario 

All hours 

112 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-15 

(n) NAAQS Scenario 

All hours 

112 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-16 

(o) NAAQS Scenario 

All hours 

112 Mm-1

 98th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-17 

(p) NAAQS Scenario 

All hours 

64 Mm-1

 90th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-18 

(q) NAAQS Scenario 

All hours 

64 Mm-1

 95th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-19 

(r) NAAQS Scenario 

All hours 

64 Mm-1

 98th percentile 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-20 

(s) NAAQS Scenario 

15 μg/m3

 annual 

35 μg/m3

 24-hour 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight PM10 Light Extinction (excluding hours >90% RH) 
F-21 

(t) NAAQS Scenario 

12 μg/m3

 annual 

25 μg/m3

 24-hour 

Displayed: Daily Max Daylight PM10 Light Extinction (excluding hours >90% RH) 

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
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G-1 

13BAPPENDIX G 

14BADDITIONAL INFORMATION ON THE EXCLUSION OF 

DAYLIGHT HOURS WITH RELATIVE HUMIDITY 

GREATER THAN 90 PERCENT 
G-2 

This appendix provides detailed information related to the exclusion of daylight hours 

with relative humidity greater than 90 percent from the design value formula for the secondary 

NAAQS scenarios based on PM10 light extinction, as discussed in section 3.3.5. As described in 

that section, these hours have also been excluded from graphical displays of the distribution of 

PM10 light extinction under current conditions and the various NAAQS scenarios, and from the 

denominator of percentages of day or hours (as in Table 4-7).4

Table G-1 shows how many estimates of 1-hour daylight PM10 light extinction were 

excluded, based both on individual hours and on days that were affected by the exclusion of one 

or more daylight hours. Phoenix was not affected at all. Among the other areas, Detroit was the 

least affected. For all areas, comparison of the percentage of hours affected to the percentage of 

days affected indicates that several hours with high relatively humidity tend to occur in the same 

day, rather than being evenly distributed across all days. For example, in Atlanta 24 percent of 

daylight hours have relative humidity greater than 90 percent, which corresponds to about 876 

hours per year (assuming there were data for every day of the year and given that on average 

there are about 10 fully daylight hours per day). However, only 80 percent of the days 

(corresponding to 292 days, if there were data for every day of the year) are affected. Thus, on 

average, an affected day in Atlanta has about 3 affected hours. The tile plots in Figure 3-12 also 

illustrate the tendency for hours with high PM10 light extinction to cluster in some days. 

Figure G-1 shows when during the daylight hours these hours with relative humidity 

greater than 90 percent occurred, prior to the their exclusion. Some but not all areas have a 

strong tendency for the affected hours to be in the morning. The counts in this figure are across 

all the days in 2006-2008 that have estimates of PM10 light extinction, not all the actual calendar 

days in that three year period. Given the regularity of the monitoring schedules, these results 

should represent year-round conditions reasonably well. However, the estimates of PM10 light 

extinction for Phoenix and Houston are not seasonally balanced due to one calendar quarter with 

no data in each case (see Table 3-4), so the true year-round time-of-day distributions of excluded 

hours for these two areas may be somewhat different than shown here. 

Figure G-2 contrasts the distribution of daylight PM10 light extinction estimates before 

and after the exclusion, based on both daily maximum values and all daylight hourly values 

individually. The differences observable in the figure are consistent with the information on the 

percentages of hours and day affected in the study areas. In most cases, the highest values of 

light extinction are notably lower after exclusion, on both a daily maximum basis and individual 

 

4

 This appendix was prepared prior to the discovery of the SANDWICH processing error noted in the 

footnote on page 3-22 of the main document, and has not been updated to incorporate that correction. Values for 

PM10 light extinction in Figure G-2 and Table G-2 are therefore slightly inconsistent with values presented in the 

main report, but this should have a negligible effect on the comparisons presented. 
G-3 

hour basis, indicating that PM concentrations in some of the excluded hours are fairly high. If 

only low-PM hours were excluded by the relative humidity screen, the highest values of PM10 

light extinction would not have been affected. 

Finally, Table G-2 contrasts PM10 light extinction design values before and after the 

exclusion, for the 90th and 95th percentile forms based on daily maximum daylight 1-hour PM10

light extinction, for current conditions. (A similar comparison for the 98th percentile form was 

not generated.) As expected, design values are notably lower after the exclusion. For both 

percentile forms, the largest reduction is in Los Angeles (represented by the Rubidoux site in the 

far eastern part of the South Coast Air Basin). Phoenix had no hours with relative humidity 

greater than 90 percent, and accordingly Table G-2 shows that its PM10 light extinction design 

values are not affected by the exclusion. Similarly, Detroit and Dallas had only a few hours with 

relative humidity greater than 90 percent, and their design values are affected very little by the 

exclusion. 

Table G-1. Percent of Daylight Hours and Days Affected by the Elimination of 

Hours with Relative Humidity Greater Than 90 Percent 

Study Areas 

Percent of Daylight Hours 

Excluded 

Percent of Days with at 

Least One Daylight Hour 

Excluded 

Tacoma 12.3 49.1 

Fresno 3.6 15.7 

Los Angeles 10.6 49.7 

Phoenix 0.0 0.0 

Salt Lake City 2.9 13.7 

Dallas 2.8 12.8 

Houston 9.6 40.9 

St. Louis 6.4 21.1 

Birmingham 4.4 19.1 

Atlanta 24.1 80.7 

Detroit 2.3 7.1 

Pittsburgh 11.4 41.2 

Baltimore 10.6 33.2 

Philadelphia 9.6 31.7 

New York 9.1 22.4 
G-4 

Figure G-1. Distribution by Time of Day of Eliminated Daylight Hours with Relative Humidity Greater 
G-5 

Figure G-2. Comparison of Distributions of Estimated Daylight 1-Hour PM10 Light 

Extinction and Maximum Daily Daylight 1-Hour PM10 Light Extinction Across the 2005-

2007 Period for Current Conditions, by Study Area, Before and After Elimination of Hours 

with Relative Humidity Greater Than 90 Percent. 

(a) Maximum Daily Values: 

Before Elimination 

After Elimination 
G-6 

(b) Individual 1-Hour Values: 

Before Elimination 

After Elimination 

 
G-7 

Table G-2. Comparison of 90th and 95th Percentile PM10 Light Extinction Design Values 

for the 2005-2007 Period for Current Conditions Based on Maximum Daily 1-Hour 

Daylight PM10 Light Extinction, Before and After Elimination of Hours with 

Relative Humidity Greater Than 90 Percent

Study 

Areas 

PM10 Light Extinction Design Values Based on Daily Maximum 1-Hour Values 

90th Percentile 95th Percentile 

Before 

Exclusion 

After 

Exclusion 

Reduction 

Due to 

Exclusion 

Before 

Exclusion 

After 

Exclusion 

Reduction 

Due to 

Exclusion 

Tacoma 244 140 104 371 157 215

Fresno 381 338 43 533 463 70

Los Angeles 919 469 450 

114

0 554 586

Phoenix 105 105 0 144 144 0

Salt Lake 

City 176 164 12 266 252 13

Dallas 189 183 5 239 239 0

Houston 253 194 59 279 234 44

St. Louis 359 307 52 423 381 42

Birmingham 366 357 9 496 483 13

Atlanta 380 249 131 462 288 174

Detroit 313 310 3 473 473 0

Pittsburgh 368 278 90 500 313 187

Baltimore 399 246 153 446 286 159

Philadelphia 382 286 96 449 339 110

New York 339 306 33 415 355 61
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H-1 

15BAPPENDIX H 

16BINTER-YEAR VARIABILITY

One aspect of a NAAQS is whether it is based on the level of the selected indicator for a 

single year, or the average of the level of that indicator over multiple years. The NAAQS 

scenarios examined in this assessment are all based on a three-year average approach. That is, 

design values are based on the average of specified percentile values of PM10 light extinction 

from 2005, 2006, and 2007. Table H-1 presents more detailed information on the variability of 

these percentiles across these three years. 

Figure H-1 presents some of the information in Table H-1 in graphical form, specifically 

for the 90th percentile form for both the daily maximum and all hour approaches. 
H-2 

Table H-1. Year-specific Percentile Values of PM10 Light Extinction for 2005, 2006, 

and 2007

Study 

Areas

90th Percentile Form 95th Percentile Form 98th Percentile Form 

2005 2006 2007 2005-

2007 

Average 

2005 2006 2007 2005-

2007 

Average 

2005 2006 2007 2005-

2007 

Average 

Based on Daily Maximum 1-Hour Daylight PM10 Light Extinction 

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 121 158 140 NA 141 173 157 NA 214 207 211 

Fresno 298 293 398 330 549 363 467 460 653 398 540 530 

Los 

Angeles 424 461 477 454 507 523 619 550 582 594 658 611 

Phoenix 100 110 NA 105 156 131 NA 144 182 187 NA 185 

Salt Lake 

City 216 112 161 163 309 142 305 252 341 191 696 409 

Dallas 184 170 197 184 252 223 242 239 312 321 271 301 

Houston 217 204 161 194 269 238 196 234 306 319 248 291 

St. Louis 350 329 239 306 432 405 303 380 483 572 347 467 

Birmingham 438 307 325 357 547 410 493 483 608 513 565 562 

Atlanta 235 255 257 249 283 295 286 288 305 338 351 331 

Detroit 300 312 313 308 347 401 664 471 391 490 1051 644 

Pittsburgh 284 257 292 278 347 272 320 313 360 350 382 364 

Baltimore 303 227 208 246 362 258 239 286 415 302 260 326 

Philadelphia 257 325 274 285 331 352 318 334 426 375 320 374 

New York 333 265 320 306 405 272 384 354 559 353 441 451 

Based on 1-Hour Daylight PM10 Light Extinction (All Hours) 

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 73 78 76 NA 101 109 105 NA 120 151 136 

Fresno 181 172 211 188 255 254 273 261 391 326 387 368 

Los 

Angeles 258 267 257 261 314 353 357 341 393 451 478 441 

Phoenix 67 68 NA 68 79 78 NA 79 92 96 NA 94 

Salt Lake 

City 116 67 97 93 193 83 148 141 255 116 304 225 

Dallas 114 100 125 113 145 126 158 143 184 176 203 188 

Houston 116 98 100 105 143 122 119 128 191 174 148 171 

St. Louis 227 195 157 193 276 240 188 235 334 309 226 290 

Birmingham 191 162 166 173 251 204 226 227 340 267 319 309 

Atlanta 166 164 168 166 188 194 202 195 233 233 248 238 

Detroit 226 212 198 212 268 252 234 251 320 312 313 315 

Pittsburgh 173 153 176 167 217 193 218 209 284 237 272 264 

Baltimore 203 161 150 171 290 190 196 225 342 225 218 262 

Philadelphia 163 203 182 183 209 234 223 222 279 298 258 278 

New York 203 169 187 186 264 222 244 243 313 267 317 299 
H-3 

Figure H-1. Inter-year Variability in 90th Percentile 1-Hour Daylight PM10 Light 

Extinction (excluding hours with relative humidity greater than 90 percent) 

(a) Daily Maximum Approach 

0

100

200

300

400

500

600

2005 2006 2007

Year

Daily Max Daylight PM Light 

Ext. (Mm-1)

Salt Lake City Dallas Houston St. Louis

Birmingham Atlanta Detroit Pittsburgh

Baltimore Philadelphia New York Phoenix

Los Angeles Fresno Tacoma

(b) All Daylight Hours Approach 

0

50

100

150

200

250

300

2005 2006 2007

Year

Hourly Daylight PM Light 

Ext. (Mm-1)

Salt Lake City Dallas Houston St. Louis

Birmingham Atlanta Detroit Pittsburgh

Baltimore Philadelphia New York Phoenix

Los Angeles Fresno Tacoma
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I-1 

17BAPPENDIX I 

18BDAYLIGHT HOURS 

Table 3-5 shows the simple scheme used in this analysis to denote hours as fully daylight 

and thus eligible for consideration in the calculation of design values for the secondary NAAQS 

scenarios based on PM10 light extinction. This scheme also has been used to select which hours 

to show in various graphics. The scheme is based on applying a fixed set of fully daylight hours 

for each three-month season (November to January, etc.). In reality, the local time minutes of 

daylight vary continuously during the year, with latitude, and with the east-west position of a city 

within its time zone. The hours that are fully daylight will change in increments rather than 

continuously. This appendix examines how well the simple scheme reflects actual conditions and 

how disparities if any might affect the results presented and the answers to policy relevant 

questions that may be addressed in the subsequent policy assessment document. 

Six study areas were selected for this examination: Tacoma, Los Angeles, Phoenix, 

Houston, Detroit, and New York. These areas cover the extremes with regard to latitude and to 

east-west position within time zone. For each area, the times of sunrise (defined by the leading 

or top edge of the sun appearing above the horizon) and of sunset (defined by the leading or 

bottom edge of the sun disappearing below the horizon) were obtained for each day of the year. 

It is several minutes after each of these times that the sun is fully visible in the morning and not 

visible at all in the evening. 

Figure I-1 shows the relationship between these sunrise and sunset times and the simple 

scheme used to denote hours as fully daylight. The vertical scale is in hours with zero 

corresponding to local noon. The smooth curves represent the actual times of sunrise (top of 

figure) and sunset (bottom of figure). The stepped lines represent the scheme used to select the 

first and last hour denoted as fully daylight. Months are indicated on the horizontal axis. The 

figure indicates that the simple scheme has the effect of treating some hours as daylight that in 

fact contain minutes prior to sunrise or after sunset, and conversely treating some hours as not 

daylight that include no such minutes. In particular: 

 In February, the hours from 7 am to 8 am and from 5 pm to 6 pm are treated as 

daylight but include non-daylight minutes in most of the example areas. 

 In April, the hour from 6 am to 7 am is treated as non-daylight but in many areas 

includes only minutes that are after sunrise. 

 In most of June and most of July, for Detroit and Tacoma only, the hour of 7 pm to 8 

pm is treated as non-daylight but in fact has no minutes after sunset. 
I-2 

 In October, the hours of 6 am to 7 am and 5 pm to 6 pm are treated as daylight but 

include non-daylight minutes in all of the example areas. 

The tile plots in Figure 3-12 can be used to assess the significance of these disparities, 

i.e., whether they are likely to significantly affect PM10 light extinction design values. Table I-1 

contains observations for each of the 24 combinations of the four time periods listed above and 

the six example areas. Taken together, these observations make it likely that refining the scheme 

for designating hours as fully daylight would not significantly change conclusions that can be 

drawn from this assessment as it has been performed. Changing the scheme would involve 

considerable effort in updating virtually every table and graphic in the assessment, however.
I-3 

Figure I-1. Comparison of Actual Sunrise and Sunset Times to this Assessment’s Scheme to Den

Daylight 

Sunrise/Sunset for Six Sites

0

200400600800

10001200140016001800200022002400

Jan10 Feb- 10 Mar- 10 Apr- 10 May- 10 Jun- 10 Jul- 10 Aug- 10 Sep- 10 Oct- 10 Nov- 10 Dec- 10

Date

Local Time

AssAssTAC TAC LA LA SPHX PHX HOU HOU DET DET NY NY S
I-4 

Table I-1. Observations from Tile Plots for Hours with Questionable Daylight/Non-Daylight Status 

Study Area February (AM and PM) April (AM) June-July (PM) OctobeTacoma The morning hour with questionable 

daylight status tends to have RH > 90%. The evening hour in question tends to either have low PM light extinction or to have RH > 90%. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but the instances of high PM light extinction that do appear typically last multiple hours. 

Very late afternoon typically is not a period of high PM light extinction. 

Instances the questiinvolve R

Los Angeles Instances of high light extinction 

involving the questionable hours are multi-hour and/or involve RH > 90%. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but the instances of high PM light extinction that do appear typically last multiple hours. 

NA Instances 

the questiinvolve R

Phoenix Instances of high light extinction 

involving the questionable hours are multi-hour. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but early morning in April typically is not a time of high PM light extinction. 

NA PM light 

October; in the quecentral pa

Houston Instances of high light extinction 

involving the questionable hours are multi-hour and/or involve RH > 90%. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but the instances of high PM light extinction that do appear typically last multiple hours and/or involve RH >90%. 

NA The amou

missing dhigh PM questionasimilar le

Detroit Instances of high light extinction 

involving the questionable hours are multi-hour. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but the instances of high PM light extinction that do appear typically last multiple hours. 

July generally is a time of high PM light extinction for the hours currently considered daylight. Adding one more late afternoon hour likely would not affect design values. 

Instances the questi

New York All but one instance of high light 

extinction involving the questionable hours are multi-hour. 

The tile plot does not show data for the morning hour that may better be denoted daylight, but the instances of high PM light extinction that do appear typically last multiple hours. 

NA Instances 

the questiinvolve R
 J- 1

APPENDIX J 

19BLOGIT MEMORANDUM 
SC11979

Memorandum

To: Vicki Sandiford, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency

From: Leland Deck and Megan Lawson, Stratus Consulting Inc. 

Date: 2/3/2010 

Subject: Statistical analysis of existing urban visibility preference studies

During the CASAC meeting on October 5-6, 2009, Dr. Bill Malm and other CASAC members 

suggested that a limited dependent variable statistical analysis could be used to analyze the 

acceptability criteria responses in the four cities for which there are existing urban visibility 

preference studies. It was the view of those Panel members that successful statistical analyses of 

the studies results would provide an estimate of a “best fit” central tendency function describing 

the results of the preference studies, as well as confidence intervals around the estimated 

functions. Such analyses would also make it possible to conduct hypothesis testing, such as 

examining whether the estimated 50% criteria level in one study is statistically different than the 

50% criteria level in another study. 

On the basis of the CASAC comments and the information available in the previous Stratus 

Report (Stratus Consulting, 2009), EPA concluded it was appropriate to conduct further 

statistical analyses on the available urban visibility preference studies. Subsequently, EPA asked 

Stratus Consulting to re-examine the data from these studies and identify several methods for 

statistical analyses along the lines CASAC members suggested. This memorandum provides a 

description of the statistical analyses we conducted, and summarizes the results. 

Data

While we do not have complete original response data from each preference study, certain data 

available in all four studies can be used to derive a set of data for an analysis comparing the 

results from each of the four1

 

1

 In the initial set of analyses discussed in this memorandum we combine the results from the 2001 

Washington, DC focus group study with all 26 participants in the “Test 1” analysis from Smith and Howell 

(2009). “Test 1” was designed to replicate the 2001 focus group study, with a goal of making two sets of 

results directly comparable. Additional analysis described later in this memorandum uses a different set of 

statistical techniques to examine the Washington, DC studies in more detail. 

cities. This available data is the percentage of respondents that 

rated each individual photograph (or image) as acceptable. We also know the total number of 

individuals that rated each photograph, as well as the haziness level in each photograph, 

measured in deciviews (dv). Using these pieces of information we were able to assemble a 

master data set of 19,280 observations from the original data. Each observation is associated 
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with an individual binary “yes” or “no” acceptability answer, the dv level, and the city location 

for a single photograph. 

For example, in the Phoenix study 385 participants rated each of 21 different WinHaze images. 

Hence the Phoenix study contributes 8,085 (385 × 21) observations, nearly 41.9% of the total set 

of 19,280 observations in the master data set. The 32 photographs used in the Denver study 

contribute 6,848 observations (35.5% of the total), the 20 photographs in the British Columbia 

contribute 3,600 observations (18.7% of the total), and the combined Washington, DC studies 

(combining data from the DC-2001 study with the Test 1 data from the DC-2009 study) 

contribute 747 (3.9% of the total). The 19,280 observations are fairly evenly split, with 9,452 

 “yes” observations, and 9,828 “no” responses. 

The participants in each study viewed a series of images with different dv levels. While the data 

collected by the original researchers included information linking each individual with their 

ratings on each picture, such detailed information is currently only available for the Washington, 

DC study conducted in 2009. Access to this additional level of information in the 2009 

Washington study allows us to conduct an additional type of analysis accounting for individual 

heterogeneity of preferences regarding acceptable levels of visibility.

Statistical Analysis Models

All of the analyses described in this memorandum are logistic regressions using the logit model. 

The logit model is a generalized linear model used for binomial regression analysis which fits 

explanatory data about binary outcomes (in this case, a person rating a photograph acceptable or 

not) to a logistic function curve. 

In the context of the preference studies, the logit model estimates the function that best 

approximates the percentage of respondents that will rate a photograph acceptable based on a set 

of explanatory variables. The observations on the dependent variable have one of two discrete 

values: 1 (the person rated the photograph acceptable) or 0 (unacceptable). In our context, the 

logit model estimates the proportion of participants who will find any particular dv level 

acceptable. In our analysis, there were two basic types of explanatory (independent) variables; 

one continuous numerical variable (the photograph’s haziness level in dv), and a set of discrete 

variables that identify which city the observation is from. We estimate two variations of the logit 

model, using the basic explanatory variables in different ways. 

The fundamental form of a logistic function is: 

. 1

1 )()"(" z e

obabilitypr yes f z − + ==

where the variable z, known as the logit, is the influence of all the explanatory variables:
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.... 2211 o xxz ++++= εβββ

In our analysis the estimated logistic function f (z) is the estimated probability of the participants 

in the study rating a photograph acceptable, given the dv value of the photograph and what city 

the observation came from. 

We conducted the logit analysis using two alternative forms of the logit model.

Model 1 is a simple form of the logit model, and includes the dv value and uses the city 

information to create a set of categorical indicator variables. This analysis assumes that all 

respondents have a similar shape to their response function (the probability function of 

responding “yes” given the dv level of a photograph), but investigates whether the location of the 

response function differs in the four cities. 

The logit for Model 1 is: 

z = Intercept dv BC DC ++++ ββββ Phoenix + ε 1 2 3 4 . 

The variables BC (British Columbia), DC (Washington), and Phoenix are the indicator (or 

“dummy” variables. For example, the BC variable is set equal to one if the observation is from 

the BC study, and set to zero if that observation is from a study in a different city study. Denver 

is used as the omitted city indicator variable, allowing the estimated coefficients on the other 

three city indicator variables to estimate if the response function is different in those cities than 

in Denver. The term ε represents the error with which the model was estimated, or the difference 

between the actual and predicted values of z. The logit model assumes that ε has a mean of zero. 

The Model 1 form of the logit model estimates a single “slope” for the response function in all 

cities as β1, the coefficient for haziness (dv). The other terms shift the intercept. The intercept 

for Denver is simply the estimated parameter Intercept. The effective intercept for the other 

cities becomes the sum of Intercept plus the coefficient on the city’s indicator variable, for 

example the intercept for Washington is Intercept + β3. 

Model 1 creates one test of the hypothesis that the responses in each city are the same. If the 

estimated coefficient on a particular city variable is statistically significant, the analysis would 

imply that the city’s response function is likely shifted relative to the Denver function, and that 

city would have a different dv value for the 50% criteria. A positive and significant city 

coefficient shifts that city’s response function to the right, resulting in the dv level where 50% 

criteria level in that particular city is higher than Denver’s.

Model 2 is a more general model than Model 1, and relaxes the assumption in Model 1 that the 

slope of the response function is the same in every city. Model 2 includes not only dv and the 
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city indicator variables as in Model 1, but also a set of interaction terms, where each city dummy 

variable is multiplied by the dv level. The logit for Model 2 is:

)( ( .)

)(

5 6 7

1 2 3 4

β β β ε

βββ β

+×+ ×+ +

= +×+++

dv DC Phoenix dv Phoenix

z Intercept dv BC dv BC DC

For example, in Model 2 the estimated total intercept for Washington becomes Intercept + β4, 

and the estimated slope of the Washington function is β4 + β5. 

In the fully interacted Model 2 a statistically significant estimate of the city indicator variable 

coefficients (β2, β4, or β6) has the same implication as in Model 1; the response function is likely 

shifted relative to the Denver function. A statistically significant estimate of the interaction term 

coefficient (β3, β5, or β7) for a particular city implies that the response function has a different 

slope than the Denver function. 

The fully interacted model produces the same results as conducting a separate logit analysis for 

each of the four cities. The interacted model, however, makes it easier to conduct hypothesis 

testing on the estimated mean response functions. 

The predicted mean dv values at each of the acceptance criteria presented here are a function of 

the coefficients on dv and the other explanatory variables, each of which have their mean and 

standard deviation. Therefore, a confidence interval constructed around this predicted mean 

must account for both the variance and covariance of the parameter estimates. Using a Monte 

Carlo estimation approach, we made 1000 random draws from the joint distribution of the 

coefficients using the mean vector and variance-covariance matrix of the parameter estimates for 

the distribution parameters. For each of these draws we then calculated the predicted mean dv. 

After removing the lower and upper 5% of the simulated values, the lower and upper end of the 

range of predicted values represent the lower and upper range of the 95% confidence interval. 

Confidence intervals calculated using this procedure are known as Krinsky-Robb confidence 

intervals (Krinsky and Robb, 1986). Because estimating Krinsky-Robb confidence intervals 

requires a separate Monte Carlo analysis for each acceptability criteria dv level, we only estimate 

confidence intervals for five different acceptability levels: 90%, 75%, 50%, 25%, and 10%. 

The Krinsky-Robb procedure assumes that the estimated parameters are normally distributed, 

which may or may not be true. To explore the potential impact of this assumption, for one logit 

analysis we also conducted an alternative procedure that does not assume a normal distribution. 

This alternative procedure (Hole, 2007) uses a bootstrap method to estimate the confidence 

intervals for the estimated mean 50% criteria. The confidence intervals using the bootstrap were 

within 1% of the confidence intervals using the Krinsky-Robb procedure, indicating that the 

multivariate normal assumption imposed by the Krinsky-Robb procedure is not unreasonable. 

We also conducted hypothesis tests using the median dv values estimated using the 
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bootstrapping procedure. The conclusions from these hypothesis tests were identical to the 

conclusions from the other hypothesis tests. 

Statistical Analysis Results, Inter-City analyses

We conducted all the logit analyses described in this document using STATA® Data Analysis 

and Statistical Software (Release ES 10.1), using the LOGIT procedure. The Krinsky-Robb 

analysis used STATA’s “wtpcikr” module. The bootstrap method (Hole, 2007) was conducted 

using STATA’s “bootstrap” module. 

Model 1 Results, Inter-City Analysis

Table 1 presents the parameter estimates from the logit analysis with city indicators (Model 1) 

which effectively shift the intercept. The Washington, DC data in this analysis includes both 

DC-2001 and DC-2009 (Test 1) data. The Denver study is the omitted indicator city in this 

analysis, so the intercept term coefficient for Denver is equal to the Constant. The intercept for 

the other cities is the sum of the constant plus the coefficient for the respective city. The 

coefficient for variable dv is the estimated slope for all four cities. 

Table 1. Model 1 logit analysis results

Variable

Coefficient 

(β)

Standard 

error z-statistic Pr |β| = 0

5% confidence 

estimate

95% confidence 

estimate

dv -0.4187 0.0059 -71.09 < 0.001 -0.430 -0.407 

British Columbia 1.1164 0.0630 17.72 < 0.001 0.993 1.240 

Washington, DC 3.8743 0.1325 29.25 < 0.001 3.615 4.134 

Phoenix 1.8021 0.0576 31.31 < 0.001 1.689 1.915 

Constant 8.3073 0.1186 70.07 < 0.001 8.075 8.540 

McFadden’s pseudo-R2 for the Model 1 estimate2

 

2 While pseudo-R2 is, like traditional R2

, bounded between zero and one, it does not have the same 

interpretation. R2 can be interpreted as the percentage of the variation in the dependent variable explained by 

variation in the independent variables. Pseudo-R2

, on the other hand, is the percent improvement in log 

likelihood from using the full set of explanatory variables, relative to a model that uses only a constant. It 

offers a sense for how much better the model fits when the explanatory variables are added, but cannot tell us 

the percentage of variation we are explaining. Pseudo R2

, instead of traditional R2, must be used in evaluating 

logit and other maximum likelihood estimation models. Similar to R2

, a higher pseudo-R2 indicates a model 

with a better fit. 

 was 0.474. 
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The Log likelihood chi2 test strongly rejects the null hypothesis there is no effect of explanatory 

variables on the probability that a respondent would find a photograph acceptable (Pr (chi2

) = 0 < 

0.000). 

The z-statistic (also known as the Wald z-statistic) in a logit analysis is analogous to the tstatistic in a conventional linear regression. The z-statistic is simply the ratio of the estimated 

coefficient to its standard error, and can be used to estimate the probability that the estimated 

coefficient is equal to zero. The column in Table 1 labeled “Pr |β| = 0” is the 2-tailed p-value 

used in testing the null hypothesis that the estimated parameter is zero. The Pr |β| values shown 

in Table 1 are all less than 0.005 ( “~0”), indicating that all of the estimated coefficients are very 

statistically significant. Because the city dummy variables are significant, in Model 1 we reject 

the hypothesis that the four studies have an identical response function. 

Figure 1 shows the estimated response functions in each city for the logit analysis with city 

indicators, as well as the underlying data as was shown in Figure 14 of the Stratus Consulting 

final report (Stratus Consulting, 2009). While Model 1 estimates the shape of a response 

function that is identical in each city, the positive and significant coefficients on the city 

variables in Model 1 result in the response functions for the different cities to shift to the right of 

the Denver function.

The logit analysis results also support estimating the dv value where the 50% acceptability

criteria are met in each city. The 50% acceptability criteria occur at the level of haziness where 

half the survey participants said the visibility is acceptable, and half said it was not acceptable. 

In Figure 1, the 50% criteria level is the dv value where the estimated response function crosses 

the 50% response level on the y axis. 

As a sensitivity analysis, it is also possible to calculate the dv levels that meet alternative 

decision criteria. For example, one can calculate the estimated dv level at which 75% of the 

participants said the visibility was acceptable. This 75% criterion would occur at better visibility 

(i.e., lower dv values) than the 50% criteria. Similarly, one can also calculate the estimated the 

dv level that any desired percentage of the participants said was acceptable. The Model 1 

estimates of alternative acceptability criteria dv values for each city are shown in Table 2.
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Table 2. Model 1 estimated haziness (dv) levels of alternative acceptability criteria

Denver British Columbia Washington, DC Phoenix

90% acceptability criteria 14.59 17.26 23.85 18.90 

75% acceptability criteria 17.22 19.88 26.47 21.52 

50% acceptability criteria 19.84 22.51 29.10 24.15 

25% acceptability criteria 22.47 25.13 31.72 26.77 

10% acceptability criteria 25.09 27.76 34.34 29.39 

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating "Acceptable"

Denver Phoenix BC Washington

Denver Logit Phoenix Logit BC Logit DC Logit

Figure 1. Estimated response functions for full cities using the Model 1 logit analysis. 
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SC11979

The range of the Model 1 estimates of the 50% acceptability criteria is very consistent with the 

Candidate Protection Level (CPL) range of 20 dv to 30 dv identified in the U.S. EPA (2009) 

report Particulate Matter Urban-Focused Visibility Assessment; External Review Draft (UFVA). 

Model 2 Results, Inter-City Results

Table 3 presents the parameter estimates from the fully interacted logit analysis, which 

investigates whether both slope and the intercept of the estimated response function differ 

between cities. Denver was again used as the omitted city in the fully interacted model. 

Table 3. Model 2 logit analysis results

Variable Coefficient (β)

Standard 

error z-statistic Pr |β| = 0

5% confidence 

estimate

95% confidence 

estimate

dv -0.3862 0.0094 -41.16 < 0.001 -0.4045 -0.3678 

British Columbia 1.0496 0.3589 2.92 0.003 0.3463 1.7530 

Washington, DC 2.9450 0.8458 3.48 < 0.001 1.2873 4.6026 

Phoenix 3.5682 0.3015 11.84 < 0.001 2.9773 4.1591 

BC × dv -0.0029 0.0162 -0.18 0.860 -0.0345 0.0288 

Wash. × dv 0.0200 0.0293 0.68 0.495 -0.0374 0.0774 

Phoenix × dv -0.0797 0.0136 -5.88 < 0.001 -0.1063 -0.0531 

Constant 7.6844 0.1830 41.99 < 0.001 7.3257 8.0431 

The pseudo-R2

 for the Model 2 estimate was 0.4756 (very similar to the Model 1 results), and the 

Model 2 log likelihood chi2 test also strongly rejects the null hypothesis there is no effect of the 

explanatory variables on the probability that a respondent would find a photograph acceptable 

(Pr (chi2) = 0 < 0.000). 

The city indicator coefficients in this full interaction model are all positive and statistically 

significant, as they were in Model 1, indicating that the response functions for different cities 

shifted right (relative to Denver). However, of all the interactions only the Phoenix interaction 

term is significant, indicating that the Phoenix response function has a different slope than the 

other three cities. 

Figure 2 shows the estimated response functions in each city for Model 2, as well as the 

underlying data.
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The significantly different slope of the Phoenix response function is clearly visible in Figure 2. 

The negative estimated coefficient on the Phoenix interaction term results in the Phoenix 

response function being steeper than the other cities’ functions. In other words, Phoenix 

respondents’ acceptance probabilities were more sensitive to changes in dv levels. Figure 2 also 

shows the Washington, DC function is modestly less steep than the others, but the decrease in the 

slope is not statistically significant. Therefore, while Washington, DC respondents are more 

likely to accept worse visibility overall, they are just as responsive to changes in dv as 

respondents in Denver and British Columbia. 

As with Model 1, it is possible to use the Model 2 results to estimate the dv values where the 

estimated response functions cross the 50% acceptability level, as well as any alternative criteria 

levels. The Model 2 estimates of alternative acceptability dv values for each city are shown in 

Table 4.

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating "Acceptable"

Denver Phoenix BC Washington

Denver Logit Phoenix Logit BC Logit DC Logit

Figure 2. Estimated response functions for four cities using the fully interacted logit 

analysis. 
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Table 4. Model 2 estimated haziness (dv) levels of alternative acceptability criteria

Denver British Columbia Washington, DC Phoenix

90% acceptability criteria 14.21 16.80 23.03 24.15 

75% acceptability criteria 17.05 19.63 26.03 21.80 

50% acceptability criteria 19.90 22.45 29.12 24.15 

25% acceptability criteria 22.74 25.28 32.03 26.51 

10% acceptability criteria 25.59 28.10 35.03 28.87 

The Model 2 estimates of the 50% acceptability criteria are nearly identical to the Model 1 

estimates; the biggest difference is a 0.07 dv decrease in the Washington, DC 50% acceptability 

criteria. The essentially identical estimates of the 50% acceptability criteria in Models 1 (city 

indicator only) and Model 2 (full interaction) indicates the choice of model form does not change 

the conclusion that the logit results are consistent with the 20 to 30 dv CPL range identified in 

the draft UFVA (EPA, 2009). 

We also conducted hypothesis testing with the four city data used in this section to examine the 

probability that the 50% acceptance criteria in the four different cities are the same. We used the 

full interaction model results for the hypothesis testing. Our approach estimated the mean 50% 

criteria dv levels and standard error (based on the Krinsky-Robb confidence intervals) for each of 

the four cities. We then conducted a hypothesis testing using a t-test to estimate the probability 

the mean 50% criteria dv levels are the same in each pair of cities. The null hypothesis in this 

hypothesis test is that the means are the same. As shown in Table 5, the null hypothesis is 

strongly rejected for all pairs of cities, indicating that the mean 50% criteria dv levels differ for 

all four cities.

Table 5. Hypothesis testing on whether the full interaction model mean 

50% criteria dv levels are the same

British Columbia

Mean dv = 22.45 

Phoenix

Mean = 24.15 

Washington, DC

Mean dv = 29.12 

Denver 

Mean dv = 19.90 

t-stat = 16.89 

Pr(Den = BC) ~0

t-stat = 35.15 

Pr(Den = Ph) ~ 0

t-stat = 30.21 

Pr(Den = DC) ~ 0

British Columbia – t-stat =12.08 

Pr(BC = Ph)~0

t-stat = 21.23 

Pr(BC = DC) ~ 0

Phoenix – t-stat = 16.53 

Pr(Ph = DC) ~ 0
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Analysis of Washington, DC Preference Studies

There are two related studies of visibility preferences in Washington, DC. In 2001, in a project 

sponsored by the U.S. Environmental Protection Agency, Abt Associates conducted a pilot focus 

group study (DC-2001) of urban visibility preferences in Washington, DC. In 2009, in a study 

for the Utility Air Regulatory Group, Smith and Howell conducted a series of three tests of urban 

visibility preferences in Washington, DC. In their first test (DC-Test 1), Smith and Howell used 

all the images used in the DC-2001 study, trying to replicate the DC-2001 study. Their second 

test (DC-Test 2) used fewer of the Washington images, restricting the study to the 12 images 

with better visibility (images with visibility of 27.1 dv or better). In the third test (DC-Test 3), 

they expanded the range of images to include two hazier images (adding a 42 and 45 dv images, 

and deleting images at 11.1, 15.6, and 24.5 dv). 

An important question is whether the participant responses obtained in the DC-2001 study are 

similar to the responses in Test 1, which was designed to replicate the DC-2001 study. A related 

question is whether the responses in Tests 2 and 3 are similar to Test 1. To investigate these 

questions we estimated logit response functions using the data from the four different 

Washington, DC data sets (DC-2001, DC-Test 1, DC-Test 2, and DC-Test 3), using the full 

interaction logit model specification.

The estimated coefficients from a full interacted model are presented in Table 6. The DC-2001 

test is used as the omitted interaction variable.

Table 6. Logit regression results with full interacted model of Washington, DC studies 

Variable

Coefficient 

(β)

Standard 

error z-statistic Pr |β| = 0

5% confidence 

estimate

95% confidence 

estimate

dv -0.4035 0.0567 -7.12 < 0.001 -0.5146 -0.2925 

Test 1 -1.5425 1.8785 -0.82 0.412 -5.2242 2.1392 

Test 2 -0.7431 2.0737 -0.36 0.720 -4.8075 3.3212 

Test 3 3.4109 2.6980 1.26 0.206 -1.8772 8.6990 

Test 1 × dv 0.0616 0.0632 0.97 0.330 -0.0624 0.1855 

Test 2 × dv -0.1043 0.0804 -1.30 0.194 -0.2618 0.0532 

Test 3 × dv -0.0607 0.0868 -0.70 0.485 -0.2309 0.1095 

Constant 11.5621 1.6777 6.89 < 0.001 8.2739 14.8504 

Figure 3 shows the estimated full interaction logit function for the separate Washington, DC Test 

data, including the DC-2001 data. 
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Figure 3 suggests that while the logit functions from DC-2001 and Test 1 appear to be similar, 

Test 2 and Test 3 appear to be substantially different. Estimating the 50% criteria levels, along 

with the Krinsky-Robb confidence intervals, confirms this observation. Table 7 presents the 

estimated mean 50% criteria levels and the Krinsky-Robb confidence intervals. 

Table 7. Mean 50% criteria levels, and Krinsky-Robb 

confidence intervals

Mean dv

5% confidence 

estimate

95% confidence 

estimate

Test 1 29.30 28.59 29.97 

Test 2 21.30 20.57 22.03 

Test 3 32.26 31.37 33.16 

DC-2001 28.65 27.46 29.70 

 

0%

50%

100%

10 15 20 25 30 35 40

dv

% Acceptable

DC-2001 Test 1 Test 2 Test 3

Figure 3. Full interaction logit results for four separate DC data sets.
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Hypothesis testing using the predicted mean dv distribution from the Krinsky-Robb procedure 

provides statistical support for the conclusion that the Test 1 and DC-2001 results are similar, but 

Test 2 and Test 3 results are different. The hypothesis testing results are presented in Table 8.

Table 8. Hypothesis testing on the individual coefficients in the full interaction model is the 

same for the four different Washington, DC experiments

Test 2

Mean dv = 21.30 

Test 3

Mean = 32.26 

DC-2001 

Mean dv = 28.65 

Test 1

Mean dv = 29.30 

Reject hypothesis that Test 1 

= Test 2 (pr < 0.001) 

Reject hypothesis that 

Test 1 = Test 3 (pr < 0.001) 

Cannot reject hypothesis that 

Test 1 = DC-2001 (pr = 0.15) 

Test 2 – Reject hypothesis that 

Test 2 = Test 3 (pr < 0.001) 

Reject hypothesis that Test 3 = 

DC-2001 (pr < 0.001) 

Test 3 – Reject hypothesis that Test 3 = 

DC-2001 (pr < 0.001) 

As shown in Table 8, we cannot reject (at the 5% confidence level) the hypothesis that the mean 

50% criteria level in the DC-2001 data and the Test 1 data are the same. In other words, it is 

likely that the mean dv in Test 1 is the same as the mean dv in DC-2001. Thus, this hypothesis 

test supports combining those two data sets together, as we did in the four city analysis presented 

above. The results in Table 8 reject the hypothesis that Test 2 and Test 3 are the same as either 

the Test 1 or DC-2001 results.

Further Analysis of the Washington, DC Test 1 Data

Smith and Howell conducted Test 1 using three distinct groups of respondents. Four of the 

respondents in Test 1 were Washington, DC area residents that were used in a pilot test of the 

testing procedure. Twelve of the respondents were CRA International employees who live in the 

Washington, DC area, and ten of the respondents were CRA International employees who live in 

the Houston, Texas area. The Test 1 participants were all shown the same images of 

Washington, DC haze levels as the DC-2001 participants, and were asked about their preferences 

for urban visibility in Washington, DC. 

We investigated heterogeneity among these three groups’ responses by conducting a full 

interaction logit analysis using information about which of the three groups (pilot, DC area or 

Houston area) the respondents were in. We also included the DC-2001 respondents (who were 

all DC area residents) in this analysis to conduct hypothesis tests on whether the Test 1 groups 

were different than the DC-2001 respondents. We used the pilot test respondents as the omitted 

group in a full interaction model analysis. The results of the logit analysis are presented in Table 

9. 
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Table 9. Logit regression results with full interacted model of the 3 Test 1 groups and the 

DC-2001 participants

Variable

Coefficient 

(β)

Standard 

error z-statistic Pr |β| = 0

5% confidence 

estimate

95% confidence 

estimate

dv -0.5719 0.1310 -4.36 0.000 -0.8287 -0.3151 

Test 1/DC -0.8344 3.7361 -0.22 0.823 -8.1570 6.4881 

Test 1/ Houston -4.8831 3.5486 -1.38 0.169 -11.8382 2.0719 

DC-2001 -2.4042 3.7273 -0.65 0.519 -9.7095 4.9012 

Test 1/DC × dv 0.1439 0.1420 1.01 0.311 -0.1344 0.4222 

Test 1/Houston × dv 0.2643 0.1372 1.93 0.054 -0.0047 0.5332 

DC-2001 × dv 0.1684 0.1428 1.18 0.238 -0.1114 0.4482 

Constant 13.9663 3.3284 4.20 0.000 7.4428 20.4898 

Using the estimated coefficients in Table 9, we calculated estimated 50% criteria levels for each 

group, along with the Krinsky-Robb confidence intervals, which are shown in Table 10. 

Table 10. Mean 50% criteria levels, and Krinsky-Robb intervals for 

the Test 1 groups and the DC-2001 participants 

Mean dv

5% confidence 

level

95% confidence 

level

Test 1/DC 30.68 29.79 31.51 

Test 1/Houston 29.52 28.30 30.66 

Test 1/Pilot 24.42 22.37 25.97 

DC-2001 28.65 27.46 29.70 

Table 10 suggests that the mean 50% acceptance criteria level for the Washington, DC area 

residents in the 2001 study are closest to the mean 50% criteria level for the Test 1 Houston area 

residents, and differ to a greater degree from the mean 50% criteria level for the Test 1 

Washington area residents. Hypothesis testing confirms this finding, as shown in Table 11. 
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Table 11. Hypothesis tests of the mean 50% acceptance criteria level for the three groups 

in the Test 1 data and the DC-2001 

Houston

Mean dv = 29.52 

Pilot

Mean dv = 24.42 

DC-2001 

Mean dv = 28.65 

Test 1/DC area 

Mean dv = 30.68 

Reject hypothesis 

Houston = Test 1/DC 

(pr = 0.06) 

Reject hypothesis Pilot = 

Test 1/DC (pr < 0.001)

Reject Test 1/DC = 2001-DC 

(pr < 2%)

Test 1/Houston 

area

– Reject Houston = Pilot 

(pr < 0.001) 

Cannot reject Houston = DC-2001 at 

5% confidence (pr = 14%) 

Test 1/Pilot – Reject Pilot = DC-2001 (pr <0.001)

These hypothesis test results in Table 11 provide some insight into the hypothesis tests in Table 

8, which found the 50% mean criteria level (mean = 29.30 dv) estimated using the combined 

Test 1 data is similar to the 50% criteria level from the DC-2001 data (mean = 28.65 dv). The 

Table 11 results suggest that the Table 8 results could be the result of the Test 1 pilot participants 

(mean = 24.42 dv) offsetting the Test 1/DC area participants (mean = 30.68 dv), giving us a 

mean estimate for the combined sample closest to the Houston area participants (mean = 29.52 

dv). 

Individual Heterogeneity

Individual respondents will likely have different general attitudes regarding haze than other 

respondents, reflecting their individual preferences about urban visibility. An individual’s 

preferences may affect how they rate the acceptability of different dv levels. In the Smith and 

Howell (2009) Washington, DC study we can track an individual’s responses over all dv levels.3

We conducted a logit analysis on Test 1 data using individuals as the indicator variable. We 

included slope interaction terms for the Washington and Houston area residents (with the pilot 

slope interaction term omitted). Each individual

 

This enables us to account for individual heterogeneity in our estimation procedure using 

individual-specific indicators. These are called fixed-effect models and control for unobserved 

differences between respondents. 

4

 

3

 While this level of data was originally collected for the studies in Denver, Phoenix, British Columbia and the 

2001 Washington, DC study, the original data is not available at this time. 

also has an indicator which becomes the 

4 Respondents 1 and 13 are dropped in the individual heterogeneity analysis because they had identical 

responses, accepting every dv level. The form of the logit model used in this analysis cannot be estimated 

when all the responses are identical. 
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intercept term for that individual. The terms for Respondents 2 through 12 are intercept shifters 

for DC respondents. Respondents 14 through 22 were Houston respondents, and Respondents 23 

through 25 were pilot respondents. The results from this model are presented in Table 12. 

Table 12. Logit analysis results of individual heterogeneity analysis 

Variable

Coefficient 

(β)

Standard 

error z-statistic Pr |β| = 0

5% 

confidence 

estimate

95% 

confidence 

estimate

dv -0.7315 0.1911 -3.83 0 -1.1060 -0.3569

Houston × dv 0.1207 0.2139 0.56 0.573 -0.2986 0.5399 

DC × dv -0.3588 0.2658 -1.35 0.177 -0.8799 0.1622 

Respondent 2 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349 

Respondent 3 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349

Respondent 4 (DC) 29.9950 5.2578 5.7 0 19.6900 40.3001 

Respondent 5 (DC) 34.3924 5.8635 5.87 0 22.9002 45.8846 

Respondent 6 (DC) 32.0347 5.5755 5.75 0 21.1070 42.9624 

Respondent 7 (DC) 31.0845 5.4326 5.72 0 20.4369 41.7322 

Respondent 8 (DC) 25.7365 4.5956 5.6 0 16.7293 34.7438

Respondent 9 (DC) 36.1200 6.1617 5.86 0 24.0434 48.1966 

Respondent 10 (DC) 34.3924 5.8635 5.87 0 22.9002 45.8846 

Respondent 11 (DC) 28.7572 5.0615 5.68 0 18.8369 38.6775 

Respondent 12 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349 

Respondent 14 (H) 16.6104 2.8047 5.92 0 11.1133 22.1075

Respondent 15 (H) 15.9236 2.7170 5.86 0 10.5984 21.2488 

Respondent 16 (H) 15.9236 2.7170 5.86 0 10.5984 21.2488 

Respondent 17 (H) 18.3145 2.9999 6.11 0 12.4348 24.1942 

Respondent 18 (H) 20.8740 3.3153 6.3 0 14.3761 27.3719 

Respondent 19 (H) 13.3405 2.3443 5.69 0 8.7457 17.9353

Respondent 20 (H) 19.8140 3.1722 6.25 0 13.5966 26.0315 

Respondent 21 (H) 16.6104 2.8047 5.92 0 11.1133 22.1075 

Respondent 22 (H) 18.8166 3.0538 6.16 0 12.8313 24.8019 

Respondent 23 (P) 16.0044 4.3526 3.68 0 7.4736 24.5353 

Respondent 24 (P) 17.1746 4.6884 3.66 0 7.9854 26.3637

Respondent 25 (P) 19.9838 5.4132 3.69 0 9.3742 30.5933 

Respondent 26 (P) 18.2301 4.9705 3.67 0 8.4882 27.9720 

As in the analyses previously described, we used the logit analysis coefficients in Table 12 to 

estimate the mean value for the 50% acceptance criteria. We also estimated the Krinsky-Robb 

confidence intervals for each data subset using the fixed effects model. Because three of the Test 
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1 participants were deleted in the individual heterogeneity analyses, for comparison purposes we 

also re-estimated a model without accounting for individual heterogeneity using the same data 

set (i.e., with the two individuals deleted). The results are presented in Table 13.

Table 13. Estimated mean 50% criteria levels, and Krinsky-Robb intervals 

for the Test 1 data accounting for individual heterogeneity

Mean dv Lower bound 95% Upper bound 95%

Washington area residents 30.57 29.97 31.18 

Houston area residents 29.40 28.41 30.33 

Pilot (DC residents) 24.40 22.60 25.91 

Mean dv estimates without individual heterogeneity (using same data)

Washington area residents 30.02 29.19 30.77 

Houston area residents 28.50 27.25 29.58 

Pilot (DC area residents) 24.42 22.37 25.97 

Table 13 shows that including individual heterogeneity in the model modestly increased the 

estimated mean 50% criteria levels.

Table 14 shows the results of hypothesis testing on the individual heterogeneity results. 

Modeling with individual heterogeneity leads to rejecting the hypothesis that the mean dv levels 

are the same in any of the three respondent groups.

Table 14. Hypothesis tests of the mean 50% acceptance criteria level 

for the three groups in the Test 1 data modeled with individual 

heterogeneity

Houston area

Mean dv = 29.40 

Pilot (DC area) 

Mean dv = 24.40 

DC area 

Mean dv = 30.57 

Reject hypothesis 

Houston = DC (pr = 0.02) 

Reject hypothesis

Pilot = DC (pr < 0.001)

Houston area – Reject Houston = Pilot 

(pr < 0.001) 
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Summary

This memorandum describes a series of logit regression analyses that estimated the percentage of 

respondents that rated a haze (dv) level acceptable in four different studies of urban visibility. 

The first analysis in this report estimated a separate logit function for each of the four studies:

Denver, British Columbia, Phoenix and Washington, DC (combining the data from the DC-2001 

study and all Test 1 data from the DC-2009 study). The estimated mean 50% criteria levels in 

the four cities (Table 4) are different, with the mean estimate ranging from 19.90 dv (Denver) to 

29.03 dv (Washington, DC). The hypothesis tests presented in Table 5 found that there is a 

statistically different logit function in each city (rejecting the null hypothesis that there was a 

single function that applies to more than one city). The range of mean estimates from the 4 city 

logit analysis is similar to the Candidate Protection Level range of 20 dv to 30 dv described in 

the draft UFVA (EPA, 2009). 

The remainder of this memorandum examined in more detail the data from the two Washington, 

DC studies. In the first analysis focusing on only the Washington, DC data, we compared the 

estimated mean 50% criteria levels from the 2001 study to the mean estimates from each of the 

three tests in the 2009 study. Figure 3 and Table 7 show the estimated mean levels in the 2001 

(mean = 28.65 dv) and 2009, Test 1 (mean = 29.30 dv) studies were similar, while the Test 2 

(21.30 dv) and Test 3 (32.26) mean levels were quite different. The hypothesis tests presented in 

Table 8 support that overall observation. The only hypothesis not rejected was the hypothesis 

that the DC-2001 and Test 1 are the same (i.e., we cannot reject the hypothesis that they have the 

same mean 50% criteria level). This finding supports our approach of combining the DC-2001 

and the DC-2009, Test 1 data in the four city analysis. 

In the second analysis of the Washington, DC data, we investigated whether the study 

participants who lived in the Washington, DC Metro area had the same mean 50% criteria levels 

as the participants who lived in the Houston metro area. This analysis involved three groups of 

Washington, DC residents (the DC-2001 participants, the pilot project participants in the DC2009 study, and participants 1 through 12 in Test 1 of DC-2009). The hypothesis tests results in 

Table 11 show that the participants in the DC-2001 and the Houston area residents in the DC2009 study are similar (i.e., we cannot reject the hypothesis they have the same mean 50% 

criteria level). Our hypothesis testing further found however, that the DC-2001 participants had 

statistically significantly different mean 50% criteria levels than either of the two groups of 

Washington, DC area residents included in the DC-2009, Test 1 results. 

The third analysis of the Washington, DC data investigated the effect of individual heterogeneity 

of preferences. This analysis was limited to the DC-2009 data because it required more complete 

information on the responses of each participant. The individual heterogeneity analysis found 

modestly higher mean 50% criteria levels than the second analysis of the Washington, DC area 
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residents. The hypothesis testing in this analysis rejected the hypothesis that the mean dv levels 

were the same for the three groups who participated in Test 1.

This apparent inconsistency with the two hypothesis tests of analyses of subsets the Washington, 

DC studies with the results of the hypothesis tests comparing the DC-2001 data with all of the 

DC-2009, Test 1data may be due to having subdivided the participants of Test 1 into subsets 

with too few members to provide stable results. Combining the DC-2001 data with all the Test 1 

data provides the largest sample size available to estimate the logit preference function for 

Washington, DC. 

References

Hole, A.R. 2007. A comparison of approaches to estimating confidence intervals for willingness 

to pay measures. Health Economics 16:827–840. 

Krinsky, I. and L. Robb. 1986. On approximating the statistical properties of elasticities. The 

Review of Economics and Statistics 68(4):715–719. 

Smith, A.E. and S. Howell. 2009. An Assessment of the Robustness of Visual Air Quality 

Preference Study Results. CRA International, Washington, DC. March 30. Prepared for the 

Utility Air Regulatory Group. Submitted as supplemental material to presentation by Anne Smith 

to the public meeting of the EPA Clean Air Science Advisory Council. April 2. 

Stratus Consulting. 2009. Review of Urban Visibility Preference Studies. Prepared for Vicki 

Sandiford, U.S. Environmental Protection Agency, Office of Air Quality Planning and 

Standards. Stratus Consulting Inc., Boulder, CO. September 21. 

U.S. EPA. 2009. Particulate Matter Urban-Focused Visibility Assessment; External Review 

Draft. U.S. Environmental Protection Agency. September. 
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United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N